{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "6799e61a",
      "metadata": {
        "id": "6799e61a"
      },
      "source": [
        "## Install (Colab Only)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa62e396",
      "metadata": {
        "id": "aa62e396"
      },
      "outputs": [],
      "source": [
        "# install\n",
        "!pip install pyepo\n",
        "!pip install gurobipy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "460e18df",
      "metadata": {
        "id": "460e18df"
      },
      "source": [
        "# Warcraft Shortest Path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ef0b563",
      "metadata": {
        "id": "1ef0b563",
        "outputId": "deb1cd53-2206-4769-e76b-2749bf0a186b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Auto-Sklearn cannot be imported.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x1ac85023990>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import time\n",
        "import random\n",
        "import pyepo\n",
        "import torch\n",
        "from torch import nn\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "# fix random seed\n",
        "random.seed(135)\n",
        "np.random.seed(135)\n",
        "torch.manual_seed(135)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22b83a43",
      "metadata": {
        "id": "22b83a43"
      },
      "source": [
        "## 1 Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0d022200",
      "metadata": {
        "id": "0d022200"
      },
      "source": [
        "We use the Warcraft terrains shortest paths [dataset](https://edmond.mpdl.mpg.de/dataset.xhtml?persistentId=doi:10.17617/3.YJCQ5S). Datasets were randomly generated from the Warcraft II [tileset](http://github.com/war2/war2edit) and used in Vlastelica, Marin, et al. \"Differentiation of Blackbox Combinatorial Solvers\". The Warcraft dataset is a captivating benchmark because the image input feature allows learning the shortest path from 10000 RGB terrains"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c4791e8",
      "metadata": {
        "id": "8c4791e8"
      },
      "outputs": [],
      "source": [
        "# map size\n",
        "k = 12"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7d8353b0",
      "metadata": {
        "id": "7d8353b0"
      },
      "source": [
        "### 1.1 Maps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a344ab63",
      "metadata": {
        "id": "a344ab63"
      },
      "outputs": [],
      "source": [
        "tmaps_train = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/train_maps.npy\".format(k,k))\n",
        "#tmaps_val = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/val_maps.npy\".format(k,k))\n",
        "tmaps_test = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/test_maps.npy\".format(k,k))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9957b4e0",
      "metadata": {
        "id": "9957b4e0",
        "outputId": "8b722e6f-2e2e-4758-b092-6b39230e7693"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.axis(\"off\")\n",
        "plt.imshow(tmaps_train[0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b903781d",
      "metadata": {
        "id": "b903781d"
      },
      "source": [
        "### 1.2 Costs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67ce1279",
      "metadata": {
        "id": "67ce1279"
      },
      "outputs": [],
      "source": [
        "costs_train = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/train_vertex_weights.npy\".format(k,k))\n",
        "#costs_val = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/val_vertex_weights.npy\".format(k,k))\n",
        "costs_test = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/test_vertex_weights.npy\".format(k,k))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9adb124",
      "metadata": {
        "id": "b9adb124",
        "outputId": "206e172a-3198-4153-aa82-0dbe83d06923"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.axis(\"off\")\n",
        "plt.imshow(costs_train[0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b723276c",
      "metadata": {
        "id": "b723276c"
      },
      "source": [
        "### 1.3 Paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87438df2",
      "metadata": {
        "id": "87438df2"
      },
      "outputs": [],
      "source": [
        "paths_train = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/train_shortest_paths.npy\".format(k,k))\n",
        "#paths_val = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/val_shortest_paths.npy\".format(k,k))\n",
        "paths_test = np.load(\"../data/warcraft_shortest_path_oneskin/{}x{}/test_shortest_paths.npy\".format(k,k))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b2a03f7e",
      "metadata": {
        "id": "b2a03f7e",
        "outputId": "53222fa3-e1b0-4d2b-e147-7678bb462013"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.axis(\"off\")\n",
        "plt.imshow(paths_train[0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3e83d132",
      "metadata": {
        "id": "3e83d132"
      },
      "source": [
        "## 2 Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0271d7a2",
      "metadata": {
        "id": "0271d7a2"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset\n",
        "class mapDataset(Dataset):\n",
        "    def __init__(self, tmaps, costs, paths):\n",
        "        self.tmaps = tmaps\n",
        "        self.costs = costs\n",
        "        self.paths = paths\n",
        "        self.objs = (costs * paths).sum(axis=(1,2)).reshape(-1,1)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.costs)\n",
        "\n",
        "    def __getitem__(self, ind):\n",
        "        return (\n",
        "            torch.FloatTensor(self.tmaps[ind].transpose(2, 0, 1)/255).detach(), # image\n",
        "            torch.FloatTensor(self.costs[ind]).reshape(-1),\n",
        "            torch.FloatTensor(self.paths[ind]).reshape(-1),\n",
        "            torch.FloatTensor(self.objs[ind]),\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2b71596c",
      "metadata": {
        "id": "2b71596c"
      },
      "outputs": [],
      "source": [
        "# datasets\n",
        "dataset_train = mapDataset(tmaps_train, costs_train, paths_train)\n",
        "#dataset_val = mapDataset(tmaps_val, costs_val, paths_val)\n",
        "dataset_test = mapDataset(tmaps_test, costs_test, paths_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "078a1328",
      "metadata": {
        "id": "078a1328"
      },
      "outputs": [],
      "source": [
        "# dataloader\n",
        "from torch.utils.data import DataLoader\n",
        "batch_size = 70\n",
        "loader_train = DataLoader(dataset_train, batch_size=batch_size, shuffle=True)\n",
        "#loader_val = DataLoader(dataset_val, batch_size=batch_size, shuffle=False)\n",
        "loader_test = DataLoader(dataset_test, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c190c011",
      "metadata": {
        "id": "c190c011"
      },
      "source": [
        "## 3 Neural Network: Truncated Resnet18"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ef5b1f76",
      "metadata": {
        "id": "ef5b1f76"
      },
      "source": [
        "Same as previous paper, we used the truncated ResNet18 (first five layers), $50$ epochs with batches of size $70$, learning rate $0.0005$ decaying at the epochs $30$ and $40$, and $n = 1, \\sigma = 1$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "043d4a35",
      "metadata": {
        "id": "043d4a35"
      },
      "source": [
        "### 3.1 Orignal Resnet18"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "892a1f3b",
      "metadata": {
        "id": "892a1f3b",
        "outputId": "6fb1abb0-23c3-4f97-a5e9-eb2b95623213"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ResNet(\n",
            "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (relu): ReLU(inplace=True)\n",
            "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "  (layer1): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layer3): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (layer4): Sequential(\n",
            "    (0): BasicBlock(\n",
            "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): Sequential(\n",
            "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (1): BasicBlock(\n",
            "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "from torchvision.models import resnet18\n",
        "nnet = resnet18(pretrained=False)\n",
        "print(nnet)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0db14ca5",
      "metadata": {
        "id": "0db14ca5"
      },
      "source": [
        "### 3.2 Truncated Resnet18"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77008efd",
      "metadata": {
        "id": "77008efd"
      },
      "outputs": [],
      "source": [
        "# build new ResNet18 with Max Pooling\n",
        "class partialResNet(nn.Module):\n",
        "\n",
        "    def __init__(self, k):\n",
        "        super(partialResNet, self).__init__()\n",
        "        # init resnet 18\n",
        "        resnet = resnet18(pretrained=False)\n",
        "        # first five layers of ResNet18\n",
        "        self.conv1 = resnet.conv1\n",
        "        self.bn = resnet.bn1\n",
        "        self.relu = resnet.relu\n",
        "        self.maxpool1 = resnet.maxpool\n",
        "        self.block = resnet.layer1\n",
        "        # conv to 1 channel\n",
        "        self.conv2  = nn.Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1), padding=(1, 1), bias=False)\n",
        "        # max pooling\n",
        "        self.maxpool2 = nn.AdaptiveMaxPool2d((k,k))\n",
        "\n",
        "    def forward(self, x):\n",
        "        h = self.conv1(x)\n",
        "        h = self.bn(h)\n",
        "        h = self.relu(h)\n",
        "        h = self.maxpool1(h)\n",
        "        h = self.block(h)\n",
        "        h = self.conv2(h)\n",
        "        out = self.maxpool2(h)\n",
        "        # reshape for optmodel\n",
        "        out = torch.squeeze(out, 1)\n",
        "        out = out.reshape(out.shape[0], -1)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fee41336",
      "metadata": {
        "id": "fee41336"
      },
      "source": [
        "### 3.3 Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60971dbc",
      "metadata": {
        "id": "60971dbc"
      },
      "outputs": [],
      "source": [
        "# number of epochs\n",
        "epochs = 50\n",
        "# learning rate\n",
        "lr = 5e-4\n",
        "# log step\n",
        "log_step = 1"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bbf12ea4",
      "metadata": {
        "id": "bbf12ea4"
      },
      "source": [
        "## 4 Optimization Model: Linear Programming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "74d187f0",
      "metadata": {
        "id": "74d187f0"
      },
      "outputs": [],
      "source": [
        "import gurobipy as gp\n",
        "from gurobipy import GRB\n",
        "\n",
        "from pyepo.model.grb.grbmodel import optGrbModel\n",
        "\n",
        "class shortestPathModel(optGrbModel):\n",
        "    \"\"\"\n",
        "    This class is optimization model for shortest path problem on 2D grid with 8 neighbors\n",
        "\n",
        "    Attributes:\n",
        "        _model (GurobiPy model): Gurobi model\n",
        "        grid (tuple of int): Size of grid network\n",
        "        nodes (list): list of vertex\n",
        "        edges (list): List of arcs\n",
        "        nodes_map (ndarray): 2D array for node index\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, grid):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            grid (tuple of int): size of grid network\n",
        "        \"\"\"\n",
        "        self.grid = grid\n",
        "        self.nodes, self.edges, self.nodes_map = self._getEdges()\n",
        "        super().__init__()\n",
        "\n",
        "    def _getEdges(self):\n",
        "        \"\"\"\n",
        "        A method to get list of edges for grid network\n",
        "\n",
        "        Returns:\n",
        "            list: arcs\n",
        "        \"\"\"\n",
        "        # init list\n",
        "        nodes, edges = [], []\n",
        "        # init map from coord to ind\n",
        "        nodes_map = {}\n",
        "        for i in range(self.grid[0]):\n",
        "            for j in range(self.grid[1]):\n",
        "                u = self._calNode(i, j)\n",
        "                nodes_map[u] = (i,j)\n",
        "                nodes.append(u)\n",
        "                # edge to 8 neighbors\n",
        "                # up\n",
        "                if i != 0:\n",
        "                    v = self._calNode(i-1, j)\n",
        "                    edges.append((u,v))\n",
        "                    # up-right\n",
        "                    if j != self.grid[1] - 1:\n",
        "                        v = self._calNode(i-1, j+1)\n",
        "                        edges.append((u,v))\n",
        "                # right\n",
        "                if j != self.grid[1] - 1:\n",
        "                    v = self._calNode(i, j+1)\n",
        "                    edges.append((u,v))\n",
        "                    # down-right\n",
        "                    if i != self.grid[0] - 1:\n",
        "                        v = self._calNode(i+1, j+1)\n",
        "                        edges.append((u,v))\n",
        "                # down\n",
        "                if i != self.grid[0] - 1:\n",
        "                    v = self._calNode(i+1, j)\n",
        "                    edges.append((u,v))\n",
        "                    # down-left\n",
        "                    if j != 0:\n",
        "                        v = self._calNode(i+1, j-1)\n",
        "                        edges.append((u,v))\n",
        "                # left\n",
        "                if j != 0:\n",
        "                    v = self._calNode(i, j-1)\n",
        "                    edges.append((u,v))\n",
        "                    # top-left\n",
        "                    if i != 0:\n",
        "                        v = self._calNode(i-1, j-1)\n",
        "                        edges.append((u,v))\n",
        "        return nodes, edges, nodes_map\n",
        "\n",
        "    def _calNode(self, x, y):\n",
        "        \"\"\"\n",
        "        A method to calculate index of node\n",
        "        \"\"\"\n",
        "        v = x * self.grid[1] + y\n",
        "        return v\n",
        "\n",
        "    def _getModel(self):\n",
        "        \"\"\"\n",
        "        A method to build Gurobi model\n",
        "\n",
        "        Returns:\n",
        "            tuple: optimization model and variables\n",
        "        \"\"\"\n",
        "        # ceate a model\n",
        "        m = gp.Model(\"shortest path\")\n",
        "        # varibles\n",
        "        x = m.addVars(self.edges, ub=1, name=\"x\")\n",
        "        # sense\n",
        "        m.modelSense = GRB.MINIMIZE\n",
        "        # constraints\n",
        "        for i in range(self.grid[0]):\n",
        "            for j in range(self.grid[1]):\n",
        "                v = self._calNode(i, j)\n",
        "                expr = 0\n",
        "                for e in self.edges:\n",
        "                    # flow in\n",
        "                    if v == e[1]:\n",
        "                        expr += x[e]\n",
        "                    # flow out\n",
        "                    elif v == e[0]:\n",
        "                        expr -= x[e]\n",
        "                # source\n",
        "                if i == 0 and j == 0:\n",
        "                    m.addConstr(expr == -1)\n",
        "                # sink\n",
        "                elif i == self.grid[0] - 1 and j == self.grid[0] - 1:\n",
        "                    m.addConstr(expr == 1)\n",
        "                # transition\n",
        "                else:\n",
        "                    m.addConstr(expr == 0)\n",
        "        return m, x\n",
        "\n",
        "    def setObj(self, c):\n",
        "        \"\"\"\n",
        "        A method to set objective function\n",
        "\n",
        "        Args:\n",
        "            c (np.ndarray): cost of objective function\n",
        "        \"\"\"\n",
        "        # vector to matrix\n",
        "        c = c.reshape(self.grid)\n",
        "        # sum up vector cost\n",
        "        obj = c[0,0] + gp.quicksum(c[self.nodes_map[j]] * self.x[i,j] for i, j in self.x)\n",
        "        self._model.setObjective(obj)\n",
        "\n",
        "    def solve(self):\n",
        "        \"\"\"\n",
        "        A method to solve model\n",
        "\n",
        "        Returns:\n",
        "            tuple: optimal solution (list) and objective value (float)\n",
        "        \"\"\"\n",
        "        # update gurobi model\n",
        "        self._model.update()\n",
        "        # solve\n",
        "        self._model.optimize()\n",
        "        # kxk solution map\n",
        "        sol = np.zeros(self.grid)\n",
        "        for i, j in self.edges:\n",
        "            # active edge\n",
        "            if abs(1 - self.x[i,j].x) < 1e-3:\n",
        "                # node on active edge\n",
        "                sol[self.nodes_map[i]] = 1\n",
        "                sol[self.nodes_map[j]] = 1\n",
        "        # matrix to vector\n",
        "        sol = sol.reshape(-1)\n",
        "        return sol, self._model.objVal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b685b7a",
      "metadata": {
        "id": "0b685b7a",
        "outputId": "751e0639-fd0f-4048-a522-98e23a8c0fc0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Set parameter Username\n",
            "Academic license - for non-commercial use only - expires 2023-07-09\n"
          ]
        }
      ],
      "source": [
        "# init model\n",
        "grid = (k, k)\n",
        "optmodel = shortestPathModel(grid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "efb15e1b",
      "metadata": {
        "id": "efb15e1b",
        "outputId": "4d304ad8-02c2-4f5a-a2b6-188c2a2d6a1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Obj: 26.998046875\n",
            "Path:\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# test\n",
        "optmodel.setObj(costs_train[0]) # assign cost\n",
        "sol, obj = optmodel.solve() # solve\n",
        "print(\"Obj: {}\".format(obj))\n",
        "print(\"Path:\")\n",
        "plt.axis(\"off\")\n",
        "plt.imshow(sol.reshape(k,k))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c4f6fee6",
      "metadata": {
        "id": "c4f6fee6"
      },
      "source": [
        "## 5 Useful Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d91dcd88",
      "metadata": {
        "id": "d91dcd88"
      },
      "outputs": [],
      "source": [
        "class earlyStopper:\n",
        "    \"\"\"\n",
        "    Early stopping for training\n",
        "    \"\"\"\n",
        "    def __init__(self, patience=3):\n",
        "        self.patience = patience\n",
        "        self.counter = 0\n",
        "        self.min_regret = np.inf\n",
        "\n",
        "    def stop(self, regret):\n",
        "        if regret + 1e-5 < self.min_regret:\n",
        "            self.min_regret = regret\n",
        "            self.counter = 0\n",
        "        else:\n",
        "            self.counter += 1\n",
        "            if self.counter >= self.patience:\n",
        "                return True\n",
        "        return False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f6f668ec",
      "metadata": {
        "id": "f6f668ec"
      },
      "outputs": [],
      "source": [
        "def evaluate(nnet, optmodel, dataloader):\n",
        "    # init data\n",
        "    data = {\"Regret\":[], \"Relative Regret\":[], \"Accuracy\":[], \"Optimal\":[]}\n",
        "    # eval\n",
        "    nnet.eval()\n",
        "    for x, c, w, z in tqdm(dataloader):\n",
        "        # cuda\n",
        "        if next(nnet.parameters()).is_cuda:\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # predict\n",
        "        cp = nnet(x)\n",
        "        # to numpy\n",
        "        c = c.to(\"cpu\").detach().numpy()\n",
        "        w = w.to(\"cpu\").detach().numpy()\n",
        "        z = z.to(\"cpu\").detach().numpy()\n",
        "        cp = cp.to(\"cpu\").detach().numpy()\n",
        "        # solve\n",
        "        for i in range(cp.shape[0]):\n",
        "            # sol for pred cost\n",
        "            optmodel.setObj(cp[i])\n",
        "            wpi, _ = optmodel.solve()\n",
        "            # obj with true cost\n",
        "            zpi = np.dot(wpi, c[i])\n",
        "            # round\n",
        "            zpi = zpi.round(1)\n",
        "            zi = z[i,0].round(1)\n",
        "            # regret\n",
        "            regret = (zpi - zi).round(1)\n",
        "            data[\"Regret\"].append(regret)\n",
        "            data[\"Relative Regret\"].append(regret / zi)\n",
        "            # accuracy\n",
        "            data[\"Accuracy\"].append((abs(wpi - w[i]) < 0.5).mean())\n",
        "            # optimal\n",
        "            data[\"Optimal\"].append(abs(regret) < 1e-5)\n",
        "    # dataframe\n",
        "    df = pd.DataFrame.from_dict(data)\n",
        "    # print\n",
        "    time.sleep(1)\n",
        "    print(\"Avg Regret: {:.4f}\".format(df[\"Regret\"].mean()))\n",
        "    print(\"Avg Rel Regret: {:.2f}%\".format(df[\"Relative Regret\"].mean()*100))\n",
        "    print(\"Path Accuracy: {:.2f}%\".format(df[\"Accuracy\"].mean()*100))\n",
        "    print(\"Optimality Ratio: {:.2f}%\".format(df[\"Optimal\"].mean()*100))\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8ca5993d",
      "metadata": {
        "id": "8ca5993d"
      },
      "outputs": [],
      "source": [
        "def plotLearningCurve(loss_log, regret_log):\n",
        "    # draw loss during training\n",
        "    plt.figure(figsize=(8, 4))\n",
        "    plt.plot(loss_log, color=\"c\")\n",
        "    plt.xticks(fontsize=10)\n",
        "    plt.yticks(fontsize=10)\n",
        "    plt.xlim(-100, len(loss_log)+100)\n",
        "    plt.xlabel(\"Iters\", fontsize=12)\n",
        "    plt.ylabel(\"Loss\", fontsize=12)\n",
        "    plt.title(\"Learning Curve on Training Set\", fontsize=12)\n",
        "    plt.show()\n",
        "    # draw normalized regret on test\n",
        "    plt.figure(figsize=(8, 4))\n",
        "    plt.plot([i*log_step for i in range(len(regret_log))], regret_log, color=\"royalblue\")\n",
        "    plt.xticks(fontsize=10)\n",
        "    plt.yticks(fontsize=10)\n",
        "    plt.xlim(-epoch/50, epochs+epoch/50)\n",
        "    plt.ylim(0, max(regret_log[1:])*1.1)\n",
        "    plt.xlabel(\"Epochs\", fontsize=12)\n",
        "    plt.ylabel(\"Normalized Regret\", fontsize=12)\n",
        "    plt.title(\"Learning Curve on Test Set\", fontsize=12)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "871b6c66",
      "metadata": {
        "id": "871b6c66"
      },
      "source": [
        "## 6 Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "637d89b1",
      "metadata": {
        "id": "637d89b1"
      },
      "source": [
        "### 6.1 Two-Stage"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "12d0c0c6",
      "metadata": {
        "id": "12d0c0c6"
      },
      "source": [
        "Two-Stage model: training with MSE of costs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96514a56",
      "metadata": {
        "id": "96514a56"
      },
      "outputs": [],
      "source": [
        "# init net\n",
        "nnet = partialResNet(k=12)\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    nnet = nnet.cuda()\n",
        "# set optimizer\n",
        "optimizer = torch.optim.Adam(nnet.parameters(), lr=lr)\n",
        "# set stopper\n",
        "# stopper = earlyStopper(patience=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "64b2d0ad",
      "metadata": {
        "id": "64b2d0ad"
      },
      "outputs": [],
      "source": [
        "# set loss\n",
        "mseloss = nn.MSELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2f758edc",
      "metadata": {
        "scrolled": false,
        "id": "2f758edc",
        "outputId": "872c186c-9920-4805-f70d-8e326457ff32"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch: 49, Loss: 0.2082: 100%|██████████████████████████████████████████████████████████████████████████| 50/50 [22:07<00:00, 26.55s/it]\n"
          ]
        }
      ],
      "source": [
        "# train\n",
        "loss_log1, regret_log1 = [], [pyepo.metric.regret(nnet, optmodel, loader_test)]\n",
        "tbar = tqdm(range(epochs))\n",
        "for epoch in tbar:\n",
        "    nnet.train()\n",
        "    for x, c, w, z in loader_train:\n",
        "        # cuda\n",
        "        if torch.cuda.is_available():\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # forward pass\n",
        "        cp = nnet(x) # predicted cost\n",
        "        loss = mseloss(cp, c) # loss\n",
        "        # backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # log\n",
        "        loss_log1.append(loss.item())\n",
        "        tbar.set_description(\"Epoch: {:2}, Loss: {:3.4f}\".format(epoch, loss.item()))\n",
        "    # scheduled learning rate\n",
        "    if (epoch == int(epochs*0.6)) or (epoch == int(epochs*0.8)):\n",
        "        for g in optimizer.param_groups:\n",
        "            g['lr'] /= 10\n",
        "    if epoch % log_step == 0:\n",
        "        # log regret\n",
        "        regret = pyepo.metric.regret(nnet, optmodel, loader_test) # regret on test\n",
        "        regret_log1.append(regret)\n",
        "        # early stop\n",
        "        #regret = pyepo.metric.regret(nnet, optmodel, loader_val) # regret on val\n",
        "        #if stopper.stop(regret):\n",
        "        #    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7735574f",
      "metadata": {
        "id": "7735574f",
        "outputId": "1ad3fa27-3253-47b2-b31a-72443f2c4c97"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:23<00:00,  1.58s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg Regret: 0.1695\n",
            "Avg Rel Regret: 0.79%\n",
            "Path Accuracy: 94.56%\n",
            "Optimality Ratio: 78.20%\n"
          ]
        }
      ],
      "source": [
        "# plot\n",
        "plotLearningCurve(loss_log1, regret_log1)\n",
        "# eval\n",
        "print(\"Test set:\")\n",
        "df1 = evaluate(nnet, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a290a155",
      "metadata": {
        "id": "a290a155"
      },
      "source": [
        "### 6.2 Baseline"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b014fa54",
      "metadata": {
        "id": "b014fa54"
      },
      "source": [
        "Baseline model: training with binary cross entropy loss of solutions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9609a0a3",
      "metadata": {
        "id": "9609a0a3"
      },
      "outputs": [],
      "source": [
        "# init net\n",
        "nnet = partialResNet(k=12)\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    nnet = nnet.cuda()\n",
        "# set optimizer\n",
        "optimizer = torch.optim.Adam(nnet.parameters(), lr=lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e33f9bc6",
      "metadata": {
        "id": "e33f9bc6"
      },
      "outputs": [],
      "source": [
        "# set loss\n",
        "bceloss = nn.BCELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "027f0153",
      "metadata": {
        "id": "027f0153",
        "outputId": "e35b1327-3d46-4f88-d98e-3602936a25d5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch: 149, Loss: 0.2599: 100%|███████████████████████████████████████████████████████████████████████| 150/150 [06:56<00:00,  2.77s/it]\n"
          ]
        }
      ],
      "source": [
        "# train\n",
        "loss_log2 = []\n",
        "nnet.train()\n",
        "tbar = tqdm(range(150))\n",
        "for epoch in tbar:\n",
        "    for x, c, w, z in loader_train:\n",
        "        # cuda\n",
        "        if torch.cuda.is_available():\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # forward pass\n",
        "        h = nnet(x)\n",
        "        wp = torch.sigmoid(h)\n",
        "        loss = bceloss(wp, w) # loss\n",
        "        # backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # log\n",
        "        loss_log2.append(loss.item())\n",
        "        tbar.set_description(\"Epoch: {:2}, Loss: {:3.4f}\".format(epoch, loss.item()))\n",
        "    # scheduled learning rate\n",
        "    if (epoch == 90) or (epoch == 120):\n",
        "        for g in optimizer.param_groups:\n",
        "            g['lr'] /= 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fab446f8",
      "metadata": {
        "id": "fab446f8",
        "outputId": "541b5596-7545-4316-d16d-49ff6d6428fe"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArQAAAGICAYAAACwbnORAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABa/ElEQVR4nO3deVxU9f4/8NcMy7Dvsikg7iC4gSIo4tLVa1ppt0UtcynTzMps9Wdd09tNs7L6lpiVWmaZt/SaqTej677mgvuGsgoDyL4PMPP5/SHMnYFhFZg58Ho+HvOoOXPOmc85c5AXn3mfz0cmhBAgIiIiIpIoubEbQERERER0LxhoiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNIYaInamW+++QYymQynT582dlOabOTIkRg5cqTR3l+j0eC7777DfffdBzc3N1hYWMDd3R0TJ07Er7/+Co1GY7S2marExETIZLJGPRITE+/pvWbOnImuXbs2a9vqn4t7bUNznTx5EpMnT4avry8UCgU8PDwQHh6OV155pVn727NnD955552WbSSRhMk49S1R+/LNN99g1qxZOHXqFEJDQ43dnCa5cuUKACAwMLDN37usrAyTJk3C77//jilTpmDy5Mnw9PTEnTt38Ntvv2HTpk3YunUrHnrooTZvmylTqVSIjY3VWzZ//nzk5+fj+++/11s+cOBAKBSKZr/XrVu3UFBQgIEDBzZ52zt37uDWrVv33Ibm2L17Nx588EGMHDkSc+bMgZeXF5RKJU6fPo0ff/wRt2/fbvI+FyxYgDVr1oC/wonuMjd2A4iofRJCoKysDNbW1o3exhhBttqiRYuwd+9efPvtt3jqqaf0Xnv44Yfx2muvobS0tEXeq6SkBDY2Ni2yL2NTKBQYOnSo3jIHBweUl5fXWl5TaWlpk66P7t27N6uNANCpUyd06tSp2dvfi1WrVsHf3x979+6Fufn/fu1OmTIFq1atMkqbiNoblhwQdVBxcXGYNm0a3N3doVAoEBAQgDVr1uitU1ZWhldeeQUDBgyAo6MjXFxcEB4ejl9++aXW/mQyGRYsWIAvvvgCAQEBUCgU+Pbbb7Vf9e7fvx/PPfcc3Nzc4OrqiocffhhpaWl6+6hZclD9dfaHH36I1atXw9/fH3Z2dggPD8eJEydqteGrr75Cr169oFAoEBgYiB9++KFRX1Onp6fj66+/xrhx42qF2Wo9e/ZEv379ANT99fWBAwcgk8lw4MABvWMKCgrCoUOHEBERARsbG8yePRuTJk2Cn5+fwTKGsLAwDBo0SPtcCIHo6GgMGDAA1tbWcHZ2xiOPPIL4+Ph6j6vakSNHMGbMGNjb28PGxgYRERHYvXu33jpN+Zyao2vXrpg4cSK2b9+OgQMHwsrKCsuWLQMArFmzBiNGjIC7uztsbW0RHByMVatWoaKiQm8fhj7L6uvuu+++Q0BAAGxsbNC/f3/s2rXL4PHpfmbVn82pU6cQGRkJGxsbdOvWDStXrqz1uVy+fBljx46FjY0NOnXqhOeffx67d++u9Xkbkp2dDTc3N70wW00ur/1reOvWrQgPD4etrS3s7Owwbtw4vV7wmTNnan9WW7Kkg0jKGGiJOqArV65g8ODBuHTpEj766CPs2rULEyZMwIsvvqgNGcDdr5NzcnLw6quvYseOHdiyZQuGDx+Ohx9+GJs2baq13x07dmDt2rX4+9//jr179yIyMlL72jPPPAMLCwv88MMPWLVqFQ4cOIAnn3yyUe1ds2YNYmJi8Mknn+D7779HcXEx7r//fuTn52vX+fLLL/Hss8+iX79+2L59O9566y0sW7aswbABAPv370dFRQUmTZrUqPY0lVKpxJNPPolp06Zhz549mD9/PmbPno3k5GTs27dPb91r167hzz//xKxZs7TL5s6di4ULF+K+++7Djh07EB0djcuXLyMiIgIZGRn1vvfBgwcxevRo5OfnY/369diyZQvs7e3xwAMPYOvWrbXWv5fPqSFnz57Fa6+9hhdffBG//fYb/va3vwG4W0owbdo0fPfdd9i1axeefvppfPDBB5g7d26j9rt79258/vnnWL58ObZt2wYXFxdMnjy5UYE/PT0dTzzxBJ588kns3LkT48ePx+LFi7F582btOkqlElFRUbh+/TrWrl2LTZs2obCwEAsWLGhU+8LDw3Hy5Em8+OKLOHnyZK2gruu9997D1KlTERgYiH/961/47rvvUFhYiMjISG1Jzttvv41HHnkEAHD8+HHtw8vLq1HtIWqXBBG1Kxs3bhQAxKlTp+pcZ9y4caJLly4iPz9fb/mCBQuElZWVyMnJMbhdZWWlqKioEE8//bQYOHCg3msAhKOjY61tq9szf/58veWrVq0SAIRSqdQui4qKElFRUdrnCQkJAoAIDg4WlZWV2uV//vmnACC2bNkihBBCrVYLT09PERYWpvceSUlJwsLCQvj5+dV5LoQQYuXKlQKA+O233+pdr+YxJSQk6C3fv3+/ACD279+vd0wAxH//+1+9dSsqKoSHh4eYNm2a3vLXX39dWFpaiqysLCGEEMePHxcAxEcffaS3XkpKirC2thavv/56vW0dOnSocHd3F4WFhdpllZWVIigoSHTp0kVoNBq9Y2rM59SQqKgo0bdvX71lfn5+wszMTFy/fr3ebdVqtaioqBCbNm0SZmZmetfTjBkzan2WAISHh4coKCjQLktPTxdyuVysWLFCu8zQZ1b92Zw8eVJvn4GBgWLcuHHa56+99pqQyWTi8uXLeuuNGzeu1udtSFZWlhg+fLgAIAAICwsLERERIVasWKH3uSQnJwtzc3Pxwgsv6G1fWFgoPD09xWOPPaZd9vzzzwv+Cif6H/bQEnUwZWVl+O9//4vJkyfDxsYGlZWV2sf999+PsrIyva/zf/rpJwwbNgx2dnYwNzeHhYUF1q9fj6tXr9ba9+jRo+Hs7GzwfR988EG959Vf3yclJTXY5gkTJsDMzKzOba9fv4709HQ89thjetv5+vpi2LBhDe6/tTk7O2P06NF6y8zNzfHkk09i+/bt2p5mtVqN7777Dg899BBcXV0BALt27YJMJsOTTz6p91l5enqif//+9fZAFxcX4+TJk3jkkUdgZ2enXW5mZobp06fj9u3buH79ut429/I5NaRfv37o1atXreWxsbF48MEH4erqCjMzM1hYWOCpp56CWq3GjRs3GtzvqFGjYG9vr33u4eEBd3f3RrXZ09MTQ4YMqdVO3W0PHjyIoKCgWjXeU6dObXD/AODq6orDhw/j1KlTWLlyJR566CHcuHEDixcvRnBwMLKysgAAe/fuRWVlJZ566im9z9rKygpRUVGN+raBqKNioCXqYLKzs1FZWYnPPvsMFhYWeo/7778fALS/YLdv347HHnsMnTt3xubNm3H8+HGcOnUKs2fPRllZWa191/eVZ3VAq1Z9p3ljbrRqaNvs7GwAd4NMTYaW1eTr6wsASEhIaHDd5qjrvFSfxx9//BHA3UCjVCr1yg0yMjIghICHh0etz+vEiRPaz8qQ3NxcCCEMvr+3tzeA/527avfyOTXEUDuSk5MRGRmJ1NRUfPrpp9rgV10j2pzrA7jb7pbaNjs7u9nXlq7Q0FC88cYb+Omnn5CWloaXX34ZiYmJ2hvDqstHBg8eXOuz3rp1a72fNVFHx1EOiDoYZ2dnbQ/d888/b3Adf39/AMDmzZvh7++PrVu3QiaTaV9XqVQGt9Ndpy1VhxJD9aTp6ekNbj9q1ChYWFhgx44dmDdvXoPrW1lZAah9HuoKHHWdl8DAQAwZMgQbN27E3LlzsXHjRnh7e2Ps2LHaddzc3CCTyXD48GGDw03VNwSVs7Mz5HI5lEplrdeqb/Ryc3Orc/uWZug87NixA8XFxdi+fTv8/Py0y8+dO9dm7WqIq6trs6+tulhYWGDp0qX4+OOPcenSJQD/+yx+/vlnvXNBRA1jDy1RB2NjY4NRo0YhNjYW/fr1Q2hoaK1HdUCUyWSwtLTUCyLp6ekGRzkwpt69e8PT0xP/+te/9JYnJyfj2LFjDW7v6emJZ555Bnv37jV4sxtw98alCxcuAID2Tvvq59V27tzZ5LbPmjULJ0+exJEjR/Drr79ixowZeuUVEydOhBACqampBj+r4ODgOvdta2uLsLAwbN++Xa/HUaPRYPPmzejSpYvBEoC2VH1t6QZzIQS++uorYzWplqioKFy6dEl7U1a16p71hhj6gwKAtmynurd83LhxMDc3x61btwx+1rrjSrdkzzlRe8AeWqJ2at++fQaH8bn//vvx6aefYvjw4YiMjMRzzz2Hrl27orCwEDdv3sSvv/6qvfO+epil+fPn45FHHkFKSgr+8Y9/wMvLC3FxcW18RHWTy+VYtmwZ5s6di0ceeQSzZ89GXl4eli1bBi8vL4NDI9W0evVqxMfHY+bMmdi7dy8mT54MDw8PZGVlISYmBhs3bsSPP/6Ifv36YfDgwejduzdeffVVVFZWwtnZGf/+979x5MiRJrd96tSpWLRoEaZOnQqVSoWZM2fqvT5s2DA8++yzmDVrFk6fPo0RI0bA1tYWSqUSR44cQXBwMJ577rk6979ixQr85S9/wahRo/Dqq6/C0tIS0dHRuHTpErZs2WK0XvVqf/nLX2BpaYmpU6fi9ddfR1lZGdauXYvc3FyjtkvXwoULsWHDBowfPx7Lly+Hh4cHfvjhB1y7dg2A4aG3dI0bNw5dunTBAw88gD59+kCj0eDcuXP46KOPYGdnh5deegnA3T+Uli9fjiVLliA+Ph5//etf4ezsjIyMDPz555+wtbXVjkJS/YfM+++/j/Hjx8PMzAz9+vWDpaVlK54JItPFQEvUTr3xxhsGlyckJCAwMBBnz57FP/7xD7z11lvIzMyEk5MTevbsqa2jBe72HmZmZuKLL77Ahg0b0K1bN7z55pu4ffu23vBepuDZZ5+FTCbDqlWrMHnyZHTt2hVvvvkmfvnlFyQnJze4vZWVFXbv3o3vv/8e3377LebOnYuCggI4OzsjNDQUGzZswAMPPADg7k1Vv/76KxYsWIB58+ZBoVBgypQp+PzzzzFhwoQmtdvR0RGTJ0/GDz/8gGHDhhnsMV23bh2GDh2KdevWITo6GhqNBt7e3hg2bFitG5pqioqKwr59+7B06VLMnDkTGo0G/fv3x86dOzFx4sQmtbU19OnTB9u2bcNbb72Fhx9+GK6urpg2bRoWLVqE8ePHG7t5AO72oB48eBALFy7EvHnzYGNjg8mTJ2P58uWYMWMGnJyc6t3+rbfewi+//IKPP/4YSqUSKpUKXl5euO+++7B48WIEBARo1128eDECAwPx6aefYsuWLVCpVPD09MTgwYP1ymGmTZuGo0ePIjo6GsuXL4cQAgkJCc2eGphI6jj1LRG1W3l5eejVqxcmTZqEL7/80tjNoXbm2WefxZYtW5Cdnc2eUSIjYw8tEbUL6enp+Oc//4lRo0bB1dUVSUlJ+Pjjj1FYWKj9SpeouZYvXw5vb29069YNRUVF2LVrF77++mu89dZbDLNEJoCBlojaBYVCgcTERMyfPx85OTmwsbHB0KFD8cUXX6Bv377Gbh5JnIWFBT744APcvn0blZWV6NmzJ1avXs0/lohMBEsOiIiIiEjSOGwXEREREUkaAy0RERERSRoDLRERERFJWoe8KUyj0SAtLQ329vZGH1SciIiIiGoTQqCwsBDe3t4NTmDSIQNtWloafHx8jN0MIiIiImpASkoKunTpUu86HTLQ2tvbA7h7ghwcHIzcGiIiIiKqqaCgAD4+PtrcVp8OGWirywwcHBwYaImIiIhMWGPKQ3lTGBERERFJGgMtEREREUkaAy0RERERSRoDLRERERFJGgMtEREREUkaAy0RERERSRoDLRERERFJGgMtEREREUkaAy0RERERSVqHnCmsIbkVFdiflwdruRzjXV2N3RwiIiIiqgd7aA24VVqKv12+jHk3bhi7KURERETUAAZaIiIiIpI0BloiIiIikjQG2noIYzeAiIiIiBrEQGuATCYzdhOIiIiIqJEYaImIiIhI0hho68GSAyIiIiLTx0BrAAsOiIiIiKSDgZaIiIiIJI2BloiIiIgkjYG2HkKwipaIiIjI1DHQGsAaWiIiIiLpYKAlIiIiIkljoK0HCw6IiIiITB8DrQEsOSAiIiKSDgZaIiIiIpI0BloiIiIikjSTCLTR0dHw9/eHlZUVQkJCcPjw4TrXnTlzJmQyWa1H3759W7xdrKElIiIiMn1GD7Rbt27FwoULsWTJEsTGxiIyMhLjx49HcnKywfU//fRTKJVK7SMlJQUuLi549NFHW6xNMhmraImIiIikwuiBdvXq1Xj66afxzDPPICAgAJ988gl8fHywdu1ag+s7OjrC09NT+zh9+jRyc3Mxa9asNm45EREREZkCowba8vJynDlzBmPHjtVbPnbsWBw7dqxR+1i/fj3uu+8++Pn51bmOSqVCQUGB3qMxWHJAREREZPqMGmizsrKgVqvh4eGht9zDwwPp6ekNbq9UKvGf//wHzzzzTL3rrVixAo6OjtqHj49Pveuz4ICIiIhIOoxecgDUrlkVQjSqjvWbb76Bk5MTJk2aVO96ixcvRn5+vvaRkpJyL80lIiIiIhNibsw3d3Nzg5mZWa3e2MzMzFq9tjUJIbBhwwZMnz4dlpaW9a6rUCigUCjuub1EREREZHqM2kNraWmJkJAQxMTE6C2PiYlBREREvdsePHgQN2/exNNPP91q7ROCVbREREREps6oPbQAsGjRIkyfPh2hoaEIDw/Hl19+ieTkZMybNw/A3XKB1NRUbNq0SW+79evXIywsDEFBQS3eJtbQEhEREUmH0QPt448/juzsbCxfvhxKpRJBQUHYs2ePdtQCpVJZa0za/Px8bNu2DZ9++qkxmkxEREREJkQmOuD36gUFBXB0dER+fj4cHBxqvX6xqAj9Tp+Gh4UF0ocNM0ILiYiIiDq2hvKaLpMY5cBUdbikT0RERCRBDLQGsIaWiIiISDoYaImIiIhI0hho68GSAyIiIiLTx0BrQGNmKSMiIiIi08BAS0RERESSxkBLRERERJLGQFsP1tASERERmT4GWgNYQUtEREQkHQy0RERERCRpDLT16ICzAhMRERFJDgOtASw5ICIiIpIOBloiIiIikjQGWiIiIiKSNAbaerCCloiIiMj0MdAawBpaIiIiIulgoCUiIiIiSWOgrQdLDoiIiIhMHwOtATIZiw6IiIiIpIKBloiIiIgkjYGWiIiIiCSNgbYerKElIiIiMn0MtAawgpaIiIhIOhhoiYiIiEjSGGjrIQSLDoiIiIhMHQOtASw5ICIiIpIOBloiIiIikjQGWiIiIiKSNAbaerCCloiIiMj0MdAawBpaIiIiIulgoCUiIiIiSWOgrQdLDoiIiIhMHwOtATIZiw6IiIiIpIKBloiIiIgkjYGWiIiIiCSNgbYerKElIiIiMn0MtAawgpaIiIhIOhhoiYiIiEjSGGjrIQSLDoiIiIhMHQOtASw5ICIiIpIOBloiIiIikjQGWiIiIiKSNAbaerCCloiIiMj0MdAawBpaIiIiIulgoCUiIiIiSWOgJSIiIiJJY6CtB2toiYiIiEyfSQTa6Oho+Pv7w8rKCiEhITh8+HC966tUKixZsgR+fn5QKBTo3r07NmzY0GLtkclYRUtEREQkFebGbsDWrVuxcOFCREdHY9iwYVi3bh3Gjx+PK1euwNfX1+A2jz32GDIyMrB+/Xr06NEDmZmZqKysbOOWExEREZEpkAkjz+8aFhaGQYMGYe3atdplAQEBmDRpElasWFFr/d9++w1TpkxBfHw8XFxcmvWeBQUFcHR0RH5+PhwcHGq9nlRWhq4nTsBKLkfpiBHNeg8iIiIiar6G8pouo5YclJeX48yZMxg7dqze8rFjx+LYsWMGt9m5cydCQ0OxatUqdO7cGb169cKrr76K0tLSOt9HpVKhoKBA71EfFhwQERERSYdRSw6ysrKgVqvh4eGht9zDwwPp6ekGt4mPj8eRI0dgZWWFf//738jKysL8+fORk5NTZx3tihUrsGzZshZvPxEREREZn0ncFFbzJiwhRJ03Zmk0GshkMnz//fcYMmQI7r//fqxevRrffPNNnb20ixcvRn5+vvaRkpLS4sdARERERMZh1B5aNzc3mJmZ1eqNzczMrNVrW83LywudO3eGo6OjdllAQACEELh9+zZ69uxZaxuFQgGFQtHk9hm5vJiIiIiIGsGoPbSWlpYICQlBTEyM3vKYmBhEREQY3GbYsGFIS0tDUVGRdtmNGzcgl8vRpUuXFmkXa2iJiIiIpMPoJQeLFi3C119/jQ0bNuDq1at4+eWXkZycjHnz5gG4Wy7w1FNPadefNm0aXF1dMWvWLFy5cgWHDh3Ca6+9htmzZ8Pa2tpYh0FERERERmL0cWgff/xxZGdnY/ny5VAqlQgKCsKePXvg5+cHAFAqlUhOTtaub2dnh5iYGLzwwgsIDQ2Fq6srHnvsMbz77rst3jYWHBARERGZPqOPQ2sMDY1rdrusDD4nTsBSJoMqKsoILSQiIiLq2CQzDi0RERER0b1ioCUiIiIiSWOgrUeHq8UgIiIikiAGWgPqmtSBiIiIiEwPAy0RERERSRoDbT1YckBERERk+hhoDWDBAREREZF0MNASERERkaQx0BIRERGRpDHQ1qMDTqJGREREJDkMtAawhpaIiIhIOhhoiYiIiEjSGGjrwYIDIiIiItPHQGsASw6IiIiIpIOBloiIiIgkjYGWiIiIiCSNgbYerKElIiIiMn0MtAbIZKyiJSIiIpIKBloiIiIikjQG2nqw5ICIiIjI9DHQGsCCAyIiIiLpYKAlIiIiIkljoCUiIiIiSWOgJSIiIiJJY6A1gDW0RERERNLBQEtEREREksZAS0RERESSxkDbACE4Gi0RERGRKWOgNYA1tERERETSwUBLRERERJLGQNsAFhwQERERmTYGWgNkMhYdEBEREUkFAy0RERERSRoDLRERERFJGgNtA1hDS0RERGTaGGgNYAUtERERkXQw0BIRERGRpDHQNoAzhRERERGZNgZaA1hyQERERCQdDLREREREJGkMtEREREQkac0OtBcuXMChQ4e0z4uKijB//nwMHToUf//739tN7Wn7OAoiIiKi9qvZgXbRokXYtWuX9vmSJUvw1Vdfoby8HCtWrMDnn3/eIg00BtbQEhEREUlHswPtpUuXEBERAeDuSADff/89li1bhrNnz+KNN97Ahg0bWqyRRERERER1aXagzcvLg5ubGwDg/PnzyM3NxWOPPQYAGDNmDOLj41umhUbGkgMiIiIi09bsQOvq6oqUlBQAwP79++Hh4YEePXoAAMrLyyVdQyuTseiAiIiISCqaHWgjIyPxzjvv4LPPPsPHH3+MCRMmaF+Li4uDj49Po/cVHR0Nf39/WFlZISQkBIcPH65z3QMHDkAmk9V6XLt2rbmHQkREREQS1uxAu2LFCshkMrz00ktQKBT4+9//rn3tp59+wtChQxu1n61bt2LhwoVYsmQJYmNjERkZifHjxyM5Obne7a5fvw6lUql99OzZs7mHQkREREQSJhP3WBuQk5MDFxcXvWUXL16Ep6cnOnXq1OD2YWFhGDRoENauXatdFhAQgEmTJmHFihW11j9w4ABGjRqF3NxcODk5NavNBQUFcHR0RH5+PhwcHGq9nl9ZCacjRwAAZSNGQCHncL1EREREbamhvKbrnpNazTBbVlaG4ODgRoXZ8vJynDlzBmPHjtVbPnbsWBw7dqzebQcOHAgvLy+MGTMG+/fvr3ddlUqFgoICvUd9WEFLREREJB3NDrRbt25FdHS09vnNmzcRGBgIW1tbREZGIjc3t8F9ZGVlQa1Ww8PDQ2+5h4cH0tPTDW7j5eWFL7/8Etu2bcP27dvRu3dvjBkzRm+Sh5pWrFgBR0dH7aMp9b1EREREZNqaHWg//PBDFBcXa5+/9tpryM3NxUsvvYRr167hvffea/S+ao4qIISoc6SB3r17Y86cORg0aBDCw8MRHR2NCRMm4MMPP6xz/4sXL0Z+fr72UT06Q2NIebQGIiIioo6g2YE2Pj4eQUFBAO6WGezduxfvv/8+Vq9ejXfffRc7duxocB9ubm4wMzOr1RubmZlZq9e2PkOHDkVcXFydrysUCjg4OOg96sOSAyIiIiLpaHagLSkpga2tLQDg5MmTUKlUGD9+PAAgMDAQqampDe7D0tISISEhiImJ0VseExOjnYWsMWJjY+Hl5dWE1hMRERFRe2He3A29vLxw7tw5jBgxAr/99ht69+6tvREsNzcXNjY2jdrPokWLMH36dISGhiI8PBxffvklkpOTMW/ePAB3ywVSU1OxadMmAMAnn3yCrl27om/fvigvL8fmzZuxbds2bNu2rbmHQkREREQS1uxA+/DDD2PJkiU4ePAg/vOf/+CNN97QvnbhwgV07969Uft5/PHHkZ2djeXLl0OpVCIoKAh79uyBn58fAECpVOqNSVteXo5XX30VqampsLa2Rt++fbF7927cf//9zT2UerGCloiIiMi0NXsc2tLSUrz88ss4duwYhgwZgs8++wzW1tYA7ta0jhkzBv/85z9btLEtpaFxzYoqK2FfNQ5tSWQkrM3M2rqJRERERB1aU8ahbXYPrbW1Nb744guDr504caK5uyUiIiIiapJmB1pdN27cQHZ2Ntzc3NrdFLQsOSAiIiIybfc0U9hPP/0EPz8/BAQEYPjw4ejTpw/8/Pzw888/t1T7jKKuMXCJiIiIyPQ0O9Du2bMHU6ZMgaOjI1auXIlNmzZpZ+SaMmUK/vOf/7RkO4mIiIiIDGr2TWHDhg2Dg4MDdu/eDbn8f7lYCIHx48ejsLAQR48ebbGGtqSGioyL1WrYHT589/8jI2HDm8KIiIiI2lRTbgprdg/tuXPnMH/+fL0wC9z9un7+/Pk4f/58c3dtUlhDS0RERGTamh1ozczMUF5ebvC1ioqKWkFXSlhBS0RERCQdzU6dgwcPxqpVq1BaWqq3XKVS4cMPP0RYWNg9N46IiIiIqCHNHrZr2bJlGDNmDLp164ZHH30Unp6eUCqV2L59O7Kzs7Fv376WbKfRNLPEmIiIiIjaSLMD7fDhw/H777/jzTffxJo1ayCEgFwuR1hYGLZs2YIuXbq0ZDvblG7JAeMsERERkWm7p0LXqKgoHD9+HIWFhUhJSUFBQQGOHj2KO3fuwN/fv6Xa2OYYaImIiIiko0VmCrOxsYGNjU1L7MokyHUmVtCw5ICIiIjIpEl3KIJWxB5aIiIiIulgoDVAr4fWiO0gIiIiooYx0Bqg10PLkgMiIiIik9akGtqzZ882ar34+PhmNcZU6AZa9tASERERmbYmBdrQ0FDIZA3PoyWEaNR6pkq37eyfJSIiIjJtTQq0GzdubK12mBw57vbOcpQDIiIiItPWpEA7Y8aM1mqHyanuo2XJAREREZFp401hdage6YA9tERERESmjYG2DmZVgVbNQEtERERk0hho62BW9V+1UVtBRERERA1hoK0De2iJiIiIpIGBtg5mrKElIiIikgQG2jpoe2iN3A4iIiIiqh8DbR2qTwxLDoiIiIhMGwNtHVhDS0RERCQNDLR10NbQGrkdRERERFQ/Bto6aIftYg8tERERkUljoK0DSw6IiIiIpIGBtg4MtERERETSwEBbB+0oB0ZtBRERERE1hIG2DpxYgYiIiEgaGGjrwJIDIiIiImlgoK3DbZUKAPCXCxcgGGqJiIiITBYDbR1yKyu1/3+uqMiILSEiIiKi+jDQNkKxmreGEREREZkqBloiIiIikjQG2kbg9LdEREREpouBthE4dBcRERGR6WKgbYQ9OTnGbgIRERER1YGBthE+SEkxdhOIiIiIqA4MtEREREQkaQy0RERERCRpDLR18FEojN0EIiIiImoEBto6jHF2NnYTiIiIiKgRGGjr8FH37nrPL3D6WyIiIiKTZBKBNjo6Gv7+/rCyskJISAgOHz7cqO2OHj0Kc3NzDBgwoMXb5GJhoff8HAMtERERkUkyeqDdunUrFi5ciCVLliA2NhaRkZEYP348kpOT690uPz8fTz31FMaMGdMm7fxvbm6bvA8RERERNY3RA+3q1avx9NNP45lnnkFAQAA++eQT+Pj4YO3atfVuN3fuXEybNg3h4eFt0s5NGRlt8j5ERERE1DRGDbTl5eU4c+YMxo4dq7d87NixOHbsWJ3bbdy4Ebdu3cLSpUsb9T4qlQoFBQV6j+ZIU6matR0RERERtR6jBtqsrCyo1Wp4eHjoLffw8EB6errBbeLi4vDmm2/i+++/h7m5eaPeZ8WKFXB0dNQ+fHx8GrVdL2trvee+x483ajsiIiIiajtGLzkAAJlMpvdcCFFrGQCo1WpMmzYNy5YtQ69evRq9/8WLFyM/P1/7SGnkVLY/Bgbqv39V24iIiIjIdDSui7OVuLm5wczMrFZvbGZmZq1eWwAoLCzE6dOnERsbiwULFgAANBoNhBAwNzfH77//jtGjR9faTqFQQNGMiRIG2tvXWhZ86hQuDRnS5H0RERERUeswag+tpaUlQkJCEBMTo7c8JiYGERERtdZ3cHDAxYsXce7cOe1j3rx56N27N86dO4ewsLBWb/PlkpJWfw8iIiIiajyj9tACwKJFizB9+nSEhoYiPDwcX375JZKTkzFv3jwAd8sFUlNTsWnTJsjlcgQFBelt7+7uDisrq1rLW8pEV1fsys7WW5ZcVgZfK6tWeT8iIiIiahqjB9rHH38c2dnZWL58OZRKJYKCgrBnzx74+fkBAJRKZYNj0rammjeGAYDfiRMQI0e2fWOIiIiIqBaZ6IB3ORUUFMDR0RH5+flwcHCod90jeXmIPHeu1vKbYWHobiDsEhEREdG9a0peM4lRDkzZcCcng8t/z8lp24YQERERkUEMtI1wcMCAWsvmx8Xho0YO/0VERERErYeBthFG1NFL++qtW3j+xo22bQwRERER6WGgvUfRaWmcbIGIiIjIiBhoG+l2eHidr9kfPoyiyso2bA0RERERVWOgbaTO9cw0VqzR4L7z59uwNURERERUjYG2CdRRUXW+drKwsA1bQkRERETVGGibQC6T1fv67uxsJJaWtlFriIiIiAhgoG2ylzp3rvO1iRcvwv/kSVRqNG3YIiIiIqKOjYG2iT7p2bPBdS4UF7dBS4iIiIgIYKBtFp96bhADgHNFRW3UEiIiIiJioG2GcS4u9b7+9PXr+Mv589iamdlGLSIiIiLquBhom+GDbt0aXOeP3FxMuXKlDVpDRERE1LEx0DaDk4UF8oYPx8bevRtcN02lgoYziRERERG1GgbaZnI0N8dML68G1+t8/DjGctIFIiIiolbDQHuPdgUHN7jOf/Py8ODFiziSl4eQ06ex6ObNNmgZERERUcfAQHuPJri6Yoi9fYPr/Zqdjchz53C2qAgf376NCo0GxWp1G7SQiIiIqH1joG0BJ0NCmryN5aFDsDt8GJGxsdAIgfzKSu1r5RoNzhUWQrD2loiIiKhBDLQt5EJoaLO2O5KfD7ODB+F05AheuXkTt8vK8LfLlzHwzBl8cvs2Qk6fxpzr11u4tURERETth0x0wG7AgoICODo6Ij8/Hw4ODi22X9mBAy22r5rEyJEAgNjCQuRWVmK0s3OrvRcRERGRsTUlr7GHViJ+yMgAAAw6cwZjzp9HclkZhBD4Nj0dFw3MTCaEYMkCERERdQgMtC2ofMQITHV3b5V9P3H1ql4P8KrkZOzMzsbMa9fQ7/RplGs02tfWpKZCfvAg/E6cgJqhloiIiNo5lhy0YMkBcLdnVH7wYIvusy4TXFywOydH+3y2pycAYEN6unbZGCcn/DFgAPIqKiCTyeBobt4mbSMiIiK6Fyw5MCKZTAYxciRGODq2+nvphlngbpDVDbPA3TFwVyUnw/noUTgdOYKKqp7c6lEVbpWWYuqVKzhXWMjeXCIiIpIk9tC2cA+trk9SUvDyrVuttv97tSUgAFOvXtU+tzczw7NeXviwRw/tsgqNBkfy8zHUwQHWZmYN7vN6SQmWJyZiiZ8fAm1tW6XdRERE1P41Ja8x0LZioAWA7zMy8E16Ov7IzW3V92lJ7/n7w9ncHM94eeHtxESsTE6GpUwGVVQUhBCITktDT2trmMlkGOnkBDOZTLut7/HjSFGp4GJujuzhw414FERERCRlDLQNaMtAW23RzZv4+PbtNnmvljLUwQEnCgq0z13MzZGjMwGErgWdO+Pj7t1hceiQdtkPAQGY6uHR6u0kIiKi9oeBtgHGCLQAsDMrCw9dutRm79fW+tva4nxxsd6yzIgIuFpYQK7Ti1uTEAIaQK+nV9fKpCSYyWSY7eUFVwuLlmwyERERmSgG2gYYK9ACQFZ5ORbExeH33Fzk1tHb2Z5906cPcisqML9zZ1jK5UgqK0PXEycAAGUjRsBCJkOlELCU371f8WxhIULOnNFuXzh8OOxaeKSGgspK/JaTgwmurrBtRJ0wERERtT4G2gYYM9BWE0JgS2YmntC5Kauj2di7N5YmJiJZpQIAeFhYoLu1NY4VFMDdwgLPenvj3aQkvW3OhoRgoL09rhQXo7NCge/S03G8oADf9ukDc7kcQghcKC5GXxsbmMsND+KhFkKvN/iv589jb24unnB3x+bAwNY7YCIiImo0BtoGmEKgrVZYWQkZgN9zcxHh4ACv48eN2h5Tt7F3b/yRm4vvMzP1lv+ja1fkVlbiVGEhDufn40kPD3wXEFBr+3cTE/F2YiKODxyIoY6OyKmogOvRo9rXq6cYvlxcjK5WVjicl4cklQpzvb2RVV4OlwbKJ4iIiKhlNCWvcZR9I7Ov+vr84U6dAAA7goIwqR3X2d6rWdevG1z+dmKi3vPNGRnobmWF+LIyfN6zJ+zNzCB01guPjcVDrq4wNxBO9+fmYvT58/C3skJCWRkAQAZg7o0buN/FBbv79WvBIyIiIqJ7xR5aI/fQGpKqUsHT0hKLbt5EsK0tzGQymMtkeDcpCTdKS43dvHbtWS8vfKlU1rtOdS+urgtFRTCXyTj2LhERUQthyUEDTD3QNkR24AAAYGdQEB6s6s39rk8fTL92zYit6jhqBtrCyko4HDkCAKgYMaLO2l1D/iwowMXiYsz29ISsnlIGjRAo1Wh40xoREXUYnPq2nauMioJqxAj4W1lplz3p6WnEFnUsHyQn4/Vbt+B59CiyKyqQVVGhfa1CCJSp1fgpMxOv3bqF4/n5GHrmDD5OSUFl1bTDfxYU4L5z53C+qAhhZ8/imevX8XsDE29EnD0Lu8OHkVFe3qrHRkREJEXsoZVgD62uxfHx8La0xAtduqBco0GlEFDI5TA/eLDWupPd3PDvrCy4WVjohTBqvqc8PDDX2xvDYmMB3L05rWY9r661PXviubg4AICruTmydYZu+6N/f4xxdgYADD97FkcLCpA3fDgczc21vfJf9+6Np728WudgiIiITAhLDhrQngJtXR66eBE7s7PRRaFAf1tbDHZwwNKuXbWvVwckAPgxMBBTrlxp+0ZSLQXDh8NeJ8ACQEJYGPxPngQAbOjdG12trPBDZiY+7N4djjpj8gohtGULWeXlUAmBzgpFm7afiIiopXCUA8I3ffpgc0YGHnN3h4elZa3Xb4eH4+8JCXixSxf0t7PDH7m5+FrnZqixzs7aYbCo7TgcOYJ/6PzhAUAbZgEgv7ISo8+fBwBYy+X4v549AQBfpqXhrYQE/N6vH9LKyzHh4sW76w8fDocGJqK4XlKCQ3l5mO3lVedsbURERKaMPbTttIe2qYQQSC8vh7K8HFdLSjDJzQ3mMhmsDh2qta6npSWs5XKMc3HBF2lp2uU/BQbi0StX4GlpiXTWeraJZ7y8MMvTU1vyUJNuGUNdqnuDv+jVC3O9vVu6iURERM3Cm8KoyWQyGbwUCgyyt8cTHh6wNTODQi6HJioKyUOH6q17Ozwc8UOHYm2vXvigWzcAwJMeHnjE3R0lkZFIqbE+tZ6vlco6wywATLx4EQmlpeh18iTWpaVBLQT6nToF2YED8Dl+HL9mZWnX/fnOHe3/3ywp0d7EBgApZWWIjI3FTzUmtLhTXo7rJSUteERERERNxx5a9tA2SnUv3l9dXPAfnYkFhBC4XFyMPjWmmr1WXIxfsrPxZnw8AOBORASul5ZieFX4+r1fPyyIi+O4uibmz0GDUKxWY1RVWcPTnp74uk8fvZpeMXIkcioq8HxcHH6sCrgr/P0x0dUVQXZ2AICDeXn4PScH73TtCnOZDDmVlXC1sGh2u0rVanyRloaJrq7oaWPT/AMkIiLJ4E1hDWCgbbqNSiU+SknBzuBgdLO2bvR2N0tKUKLRoF9V0MmtqIC1XA4rMzOoNBq9koanPT2xPj29xdtOTTPdwwPfZWRon8/39ka0TmnJxdBQBJ8+bXDb6jF6qwPwpz164Gh+Pv515w6s5HLEhoSgTzMmn1gSH4/3kpP13oOIiNo3lhxQi5vl5YVLQ4Y0KcwCQA8bG22YBQBnCwtYVU0OoJDL4VHVaxdiZ4ev+/TBtSFDMNbZGX9zc9PbTzcrK2wOCLjHo6DG0A2zAPTCLIA6wywA3C4rw2ydCT7eSUzEv6pKGco0Ggw5exZTLl/Gxhqzsf2QkQHZgQPYqFQipawM36anI0unDvtIfn6zj4eIiNo/9tCyh9aobpSU4LPUVLzu4wMfnYkiCior4Vg1+xYAlERGQiGXY0FcHErUakT36gXbw4cBAI5mZshXq+t8j/f8/fH/EhJa7yCo2R5wdcUH3bujz59/Gny9YsQIfKVUYn7V2L1Awz20pwsKEFdaiqkeHtpl2RUV2JOdjYc7deJsa0REEsGSgwYw0EpDZnk5Rp87h1leXnjFx6fW6zdLSvDvrCw86+2N127dQndrawTZ2uI+Z2dtKcMDrq7YERQEpyNHUKgTetVRUfg8NRWbMzI4NJkJe93HB6tSUvSW/V+PHnjx5k38t39/jK4aweH7jAwsS0zEc97eWHTrFgBgZbdueMPXFwAw+MwZnC4s1NYE1ySEwNsJCQiytcUUnSBMRETGw0DbAAba9q+6hnOiqyt+DQ5GsVqNr5VK+CkUCLK1RQ+dG4t0b3giafklKAibMzLwk84IDbrOhoTgtkqFBy9dAgDYmZkhb/hwbM3MRLlGgwfd3GAjl+PVW7ewpqq0gjW6RESmgYG2AQy07d+869exTqnEn4MGYXADn/FTV6/Wqht91ccHH6ak4OvevTHdwwNr09Kw8ObNOvdhI5ejRGeYKzJdD7q6Ymd2tvb539zcsE1n+LLiyEgsiItDD2trLPb11c6+pquwshIOVSUxjQnAaiFwW6WCn05ZDRER1U9yN4VFR0fD398fVlZWCAkJweGq2khDjhw5gmHDhsHV1RXW1tbo06cPPv744zZsLUnBF717oygyssEwCwBf9uqFRzt1AgCE2ttDHRWFD7p3hzoqCk97ecFSLscLnTvjwIABKBg+3OA+/tW3b5Pat7jqq3Bqe7phFoBemAWA1Skp2JiejiUJCZAfPKhdvi83F3ElJbhWXKwNswAwIjYW82/cwKDTp5GqUhl8zyeuXEHXEycw4+pVfJicDNmBA3hRpy6YiIjujdF7aLdu3Yrp06cjOjoaw4YNw7p16/D111/jypUr8DXwSz82NhbXrl1Dv379YGtriyNHjmDu3Ln4+OOP8eyzzzbqPdlDSzUJIfBnYSGCbG0bvGmoukQhxM4OZ4qK8ELnznjFxwddT5zQrqN7I1pXKyucHDQIHseOAQB2BAXhITc33Cotxd6cHCy6eROqGj+GBwcMQNS5cy13gNRsD7m64pcaIbg+YuRIrEpOxhvx8Zji7o4tgYF1lrWURkbCyswMN0tKoCwvRy8bG7hbWCCxrAxdrawM9g4TEXUUkio5CAsLw6BBg7B27VrtsoCAAEyaNAkrVqxo1D4efvhh2Nra4rvvvmvU+gy0dC8+v30bX6SlIaZ/fzibm2uHITtVUID9eXkId3DAEAcHfJ6ainHOztrJBi4VFeFcURGe8PDQCyrxpaV4+eZNbc/hZDc3bA8KYm2vRG0JCMDUq1e1z68MHozAU6cMrnt9yBDsyc7Gy1U3sula4uuLf/j7Gwy1aiFwvaQEATY2jQq9Qgj0PHkSEY6O2MTh74hIIiQTaMvLy2FjY4OffvoJkydP1i5/6aWXcO7cORzU+bqvLrGxsRg/fjzeffddPPPMMwbXUalUUOl8FVhQUAAfHx8GWjIp82/cwIG8PFwePBgymQw5FRW4WFwMfysrPHn1Kg7rjMX6frdu6G1jgw+Sk3G0oKDe/f4rMBD/LyEBNzkrm+S84eODed7eSC0vhxAC4Y6OUGk0WHTzJtYplXjYzQ3bgoIMbjvn+nVcKi7GsYEDsTQxEf9ISgJwdyg03Vn96qLSaJBUVoZeNjYoVavx8s2bmOTmhr+6urboMRIR1aUpgda8jdpkUFZWFtRqNTxqDJPj4eGB9AZmjOrSpQvu3LmDyspKvPPOO3WGWQBYsWIFli1b1iJtJmot0b166T13sbBAlJMTAODQwIFIKC3FhIsXsahLFzzj7Q3g7g1OaiGwPy8PaiEw/uJFAHfLHBLLygAAnSwscHzgQHSqKnkg6Xg/JQXv1xi2TNf2rCysrxq9Y1NGBhLKyrCvf3+UaDT4umryiseuXMHPOqNAXC8txdH8fNibmWGqhweEEDhTWIjlSUmY7OaGWV5e0AiByNhYnCosxM6gIBzNz8c6pRLrlMpG3QRXplZjf14eopycYFP1DcaNkhLsy83F015esGhEoG6IEIIlGUSkZdRAW63mP0qN+Yfq8OHDKCoqwokTJ/Dmm2+iR48emDp1qsF1Fy9ejEWLFmmfV/fQEkmJv7U1rgwZordMJpPBXCbDX1xcAAAZERGwlMngZGGB79LTcaWkBFFOTrV+nnYFB2NiVfj1USiQUsfNTGT6nrl+Xe95z5MnkaTzef5cY0iziLNnUVA1JvMEV1dsSk/HC1UjePyanY2EsjJtby4AvBkfjyslJdrnFRoNLORyqDQapJeXw8/KCpUajbbX93ZZGXyq6skVMhlKRoyAXCZD76rJM4o1GoPjStcUnZqKFcnJ+KN/f/TWGWYPAPIrKzHo9Gk84OqKT3r2bHBfRNT+GXWUAzc3N5iZmdXqjc3MzKzVa1uTv78/goODMWfOHLz88st455136lxXoVDAwcFB70HUHrlbWsKpajrh6Z6eWNGtW60wKwMwTOdn4HxoKH6p8bV18tChes/71AgUZLqSGvjjpEBnghHHI0fwRny83uu6YRaAXpgFgJdu3sTR/HxYHTqEridO4J9JSbA6dAiRsbG4XlKCp3SmPlYJgejUVL3tD+flAQAuFxdjWWIiiiora7WxUqPB83FxuK1SYf6NG1BpNHjo4kVMuHAB5RoNvkxLQ3xZGT6tsW8i6riMGmgtLS0REhKCmJgYveUxMTGIiIho9H6EEHo1skRUNydzczhZWCBl6FBkDRsGZwsLPOjmpn19vrc3fKyscHTgQBwaMAA3hgzB1SFDsLdfP+06PwUG4pGqoc66NWJs1Sfc3bX/fy40tAWPhu5VU8dPXpuWhuGxsdrnbyUkQA3gSH4++vz5J/ZXBdZqL9y8qXeD43/z8pBVXo6gU6fwTmKidnzn80VFKKsK24PPntWuXy4E3rh1Czuzs7EnJwffpqfjao2QTURk9JKDRYsWYfr06QgNDUV4eDi+/PJLJCcnY968eQDulgukpqZi06ZNAIA1a9bA19cXfaqmrzxy5Ag+/PBDvPDCC0Y7BiIp+K1fP7xy8yY2VP3sdKkRRC8NHoyfMjO1XwdHODrqvT7WxQWf9+yJ4/n5mNypEx50c8NiX18MsLNDUlkZPC0tsS4tDUsSEvBxjx7wVSi0Nb1f9e6N7zMzAdydhCKmXz/85cIF7b4jHR31bnqj9qtIrdar516fno71Ot/S7QoOxrmiIr1tvqiaxQ0ALhQXY2ONb/VulpRgVUoK/Kys8EqXLtqRR4io4zD6sF3A3YkVVq1aBaVSiaCgIHz88ccYMWIEAGDmzJlITEzEgaq/8D/77DOsW7cOCQkJMDc3R/fu3TFnzhzMnTsX8kbeaMBhu4haj1oImFWVOZwrLISnpSWcLSxgdegQACBuyBD0sLFB4J9/anvaxMiRSCgtRbeTJ2vt71kvL3zWsycUVdsT1WeOlxe+7N3b2M0gohYgmWG7jIWBlqhtVWg0sKwKpMrwcHgqFBhw6hTOFxcD+N/0sfmVlXCqmoXraU9PvOnrix42NnrbV3vNxwcfpKRgmrs7fsjMxI6gIEy6dAkA0NnSEl4KBU4XFrbREZIpacxIDERk+iQzbBcRdQwWcjm+7NULJRoNPBUKAMAcb28siIvTu0HN0dzcYBixkMuxoXdvLIiL09Z8rureHSu7dYNcJsP3gYHQ/dv85759YS6T6dVi1keMHImJFy5gd06Odtnanj3xHKenJSKSBAZaImoTc6rGzq32nLc3BtnZoX/VTGoNmeXlBX8rK4w6fx52VTWScp0RHGQyGcTIkShTq2FlZobkqnF4q73p64u/+/nhYnExBtrZaXt8F3bpAgDY1a8f0lQqvH7rFia6umKKh0eTAu2qbt3weo0RA8g4TuTnY2iNGnAiat9YcsCSAyJJOVVQgG7W1nCtGp6sPu8lJUEtBPra2mKiqyssderst2Zm4vuMDHwXEABHc8N/269OScErt27h+pAhSCorw1idG9l0HR04EBGOjjiYlwdHMzMMsLfn1MVGVhIZibk3buANX1/0tbU1dnOIqBlYQ9sABloiao5uJ04goarnd2Pv3phVNamBoTKJLRkZmHb1KgAga9gwhJw+rR0jdoaHB9b17o0VSUmILyvDGGdnzNQZv5VaVmvU1AohUCGE3h9Jui4WFWHypUv4h78/pjYwrjoAFFVW4rm4ODxaNYJItRK1WjvbGlFH05S8ZtRxaImIpOSXoCAMtrfH3n79MNPLCwcGDEBaeLjBdad6eOD6kCEojYyEq4UFtgUFoauVFbYGBuKbgAAo5HK84++PTQEBmOHpifiwMO22H3bvjgydsbj/ExzcYNu+qRqOzZCujRgruKOq1GgghNA+7pSX17luUlmZtlbb5/hxKA4dwtbMTBysMfYuADx59SpulZVh2tWr+DUrCxohsD83F1eqboSs6b3kZGzOyMBDVTc2AsC/79yB7eHD+DA5+d4OspFyKyrw+q1bONtKN1MKIXC1uBiajtePRm2APbTsoSUiE1FdphDTrx/uc3FBQmkpLhcXY6KbG8acO4d9OsFpqIMDThQUAAC+6tULz3h711nmIEaORJpKhc7HjwO4O3mGDMAanfFd3SwskFVR0RqHZXQnBg1CsK0tbMzMUFRZicmXL2OkkxNyKiqw+vbtWuvvCg7GX11cUKHRaMe0/b/bt/FS1SQQ3/bpgxk1etT72thgpqcnXvX1BQD4Hj+uN6W0k7k58qpmRTPUYzz96lVszsjQe93+8GEUVU02YWib22Vl8FYoIJfJUKJW4x9JSZjk5oYwA7/X9mRnY9udO1jZrRtszcxq9frqTllc1/vVtDMrC92srBDUyDr40efOYX9eHh5ydcWOev5IE0JAA2iH/6u2KysLZ4qK8Hc/v1ozIJqyCo0G27OyMNLJCR6WlsZujlGcKSyEq7k5ulpbN2k7jnJARCRB3wcE4EJREcY4OwMA/K2t4V/1C+Dnvn3hcvQoAGCcszO+6dMHc2/cwN86dcJTnp56+zkfGorhsbEoVKsRZm8PAPCuGl0CABZ07oye1tbaQGuGuyFtQtVEGNUKhg/HeqUSL9+6VautPwQEaEsqTN1QA6Nd/JGbW+f6E3XOw5+DBsHJ3FwbZgHUCrMAcLmkBK/Fx+NrpRLXS0trvZ6nM8VvqVqNFcnJeNDVFZeKi7EzOxulOjO2nS0sRHp5uTbMAsCt0lL8kpWF57y9YW1mhl+ysjDp0iU80qkTfurbFx+kpGBlcjJWJicj0MYGc7y8kFZejh8zM9HT2lr7x9CG9HSYASiPitK7qXJVSopee08WFKBSCEQ4OGBzRgaeunYNwxwccGTQIHyUkoJXda4JMXIkNEIgTaWqNWGLEAKv3LqFrlZW2lnkfsnOrnV+cioq4GhuDjOZDGMvXEBKWRkuDh4MC52Sjgeqeq8H2tnplWXUpVKjwZKEBIx0csJ4V9cG12/Iobw8/J6Tg6Vdu+q1y5CCykrcVqkQaGuLlcnJ+HtiIjpbWuJ2E2ZBNWUVGg0Sy8rQs45p0UvUaljKZDCXy5FQWorQM2cAtO6QeuyhZQ8tEUlEdQ/sOGdn/Na/f63XD+bl4bZKhSc8PHCtuBifpaZisa+vNmT8kZMDZXk5plcF4JSyMvwjKQkvdu6MIDs7PHX1KqzlcuRVVmKSm5u29tNQz++diAjtjF8vdO6Mz1JTW+GI26fqsZOba7anJzbozJb2eKdO2HrnTpP2cWrQIHgrFLCWy7V/KBnSycICd3R67jMjIuCuM9MbAOzt1w8fpaTg99xcrOrWDa/5+iImJwepKhWKNRosMDBaiG6wuVpcjMBTp+BoZob0iAhYHz4M4O4fE4N1fkdXX4fRPXviuc6dAdwNTnLA4OxwG5RKPK1T5/5LVhbiS0vxctVsiNVulJRApdGgr62tXsivqfr9P+nRAy9VjY6SW1GB7VlZGO7oiN5V4U4IAfnBgwCA97t1w9bMTJytmv2u+ri3ZGTgWEEBPunRo1ZPdF2yysuxKzsbj3TqBLs6bmRtqqLKSuzLy0OIvT1+y8nBo506waFq3yVqNWwPH8ajnTrhX337Iqu8HAVqNbpZW2Pc+fP4PTcX/woMxKM6U5sDQGFlJRyOHEGAjQ2uDBmCHzIy8ETVH79zvLzwlIcHhjs5Nap9vCmsAQy0RCRFz9+4gei0NJwYNMjg18qtpfoXeUJYGLooFCjRaOBgbo4fMzKwKSMDmwMC4KoTiu5zdtbrAX3VxweRjo569aHVvC0t0dfWFg+5uekFn65WVkisMfTaCEdHHDIwRfIYJyf810AdKxnHvv79Mfr8+QbXOxcail1VpRCxVYFPtzTjeW9vLPf3hxzA0sRE/F/VH00zPT2xsU8flKnV2vA7x8sL1nI5lnXtCkdzc1QIgfeSkrAsKQkAUDh8OOyrJm05HRKCEHt7bT10dfgEgG5WVrgZFqYtabhVWord2dlQC4FFVb3Sz3t74+MePbApIwPvJCbidlVpybUhQ/BnQQH+X0KCdllNFSNGwFwu1/5MfdqjB+Z7e+OthAQE2NriQVdXnCgoQH87O0y8eBGxRUXIjIhAJ0tLDD5zBqcLCzHDwwNv+fnhXFER/tapE2QyGRJKSzHr2jWkl5fj5759G1UGkldRAecaf8xMdnNDlJMTUlUqfKDTa/9TYCAevXIFAJAaHq4tX4p0dMShgQMBABohcKawEEfy87XnqmD4cDhUnXdd+cOH453ERDzu7l7vv2UMtA1goCUiqSpWq2Hbxne9XysuRnZlJYbVM7brpaIiBJ8+DQAoGzECaSoVnMzNsT8vDxNcXaGQy3GtuBj25uawlcuxJi0NA+zscL+LizY87M/N1QahX4OCtF8xA8D63r0x28sLZwoLtV9fAsDNsDB0t7bGh8nJeI3jAHcYc7y88JVSWWu5u4UFMptQC/6Mlxe+NrAfALCSy1GmUwqiy1ehQHKN0DrR1RW7DJRT1KRb/95Yb/j44P2qgKmQyaCqim5v+PigSK3Wq4cH7vYE3ywpwbmiIjzo5ob/u30bBWo1Amxs0N/ODoG2ti06tGCAjQ1meHrizWb8DGYNG1bnMIwMtA1goCUiank5FRVQyOX3HLgLKythb26ORTdv4ou0NFwaPBjdatxMIoSASuemrZ8yM/FYVQ8SUUcXYmeHM1W93oZ80qMHFurUhRvTGCcn/DFggMHXGGgbwEBLRCQNlRoNzBu4AQe4exOToZu/iMj01XWzGMehJSKidqExYRYAwhwcMLvGaA9E1HEw0BIRUbuwvk8fnA4JMXYziMgIGGiJiKjdCLG3hyYqCr2aOIA7EUkbJ1YgIqJ2RSaT4XpYGO6Ul6NUo8ELcXHY2Yi7z4lIuthDS0RE7VInS0v4Wllha2Ag9vXvj/IRI1ASGVnvNo926oT7nJ3xfrduess+7dFD+3yWpye668yINcDODo906tTyB0DUQWhaYHwC9tASEVG7ZmVmhlFV0wlbALgYGooDeXmwlMsx98YN7XqzPT2xvk8f7fM5Xl4oUKvhq1BAJpNBBuC2SoX3unWDmUyGYrUaR/LzMcrJCZZVN681NLbn7uBgqIXA4fx8bExPR1ZFhd5YqOt69UJfW1vkV1bCRi7HqDomKFjTsyee15mIoigyEh5Hj6K4jnFTk4YOhY1crp3drT4+CgVS6pgYgKg11DdDW2Nx2C4O20VE1GF9nJICH4UCFnI5xjo7w/oex9A9kZ+P8NhYAMBnPXog3NFRbyII3eGJlCoV9uXl4dFOnZBbFWDt65nS9N937uDhy5e1+/k6LQ3RaWn4NTgYnRUKxJeWovvJkwDuzux0smrWpt3BwXCpGrj+YF4eRp47h5FOTjhQY3a1nGHD4Fy1nu6+qmVERMDNwgIblErEFhXBz8oKb9QYSH9dr17aPxJ0JwNojGe9vLCud28AwNQrV/BjZibOhYbiSH4+FsTFIdLREYcNzBRH0tcSw3Yx0DLQEhFRC8oqL8epwkKMdXGBmUyGvTk5eOrqVWzo0wcTXF3vad+5FRWwNzOrdzgzjRD19niVqdVQyOX4Z1IS3k5MBADcGDIEPW1s9NY7kZ+PTpaWuFFSgjKNBpNrlFWoNBpYHTqkfb6sa1f8vWtXZJaXI660FMMcHSGEwIcpKXg9Ph5jnZ2xsls3DDpzBqOcnPBlr174LiMD8zt3hoelpd6+hRAoVqthVxXw1UJADmDixYuwMTPDz3fuNOZ06Rnn7IwbpaVIqDGl8spu3fRmuKoO9ukqFcZduIALxcX4tk8fOJmbG5y+uT5nQ0LQ3doaa9PS6p1FqyV6xUsiI2FTNRVwtc0BAdAIgV42Nvjs9m3kVVbix8BA7TTADRlkZ4ezVRM0PN6pE7Y247xX29C7N84XFeHTqimMq/koFEgODze4DQNtAxhoiYioLQkhtFP8mhKNEJAB99S2a8XFiMnNxbPe3lAYCNpCCJwpLESQra12Zrd7lVRWhjSVCuGOjsivrISjuTmEEJh17Rq8FQq8162btvzDw8ICqRERMJPJkFVejnk3bmCSmxsyKiowzd0dXgoFgMZN4lGzpCS6Z098lJKCNb16YZyLC4QQOFlQAIVcjkBbW73zYXPoEEo1GsgAaEaOxNnCQqiFwGCdHFJXycqvQUEoUKsxxN4eeZWVmHX9Oi4VF+utI0aORFxJCR67cgVv+vricXf3Oo/jZEEBfsjIwP/phMtOFhZIHjoUCrkc69LS4K1Q4EE3t7v7rrp+VRoNLGQy/DMpCf3t7PCAq6v22hFC4FJxMXrb2EAAsJTJcKG4GANOn8bu4GDcX+OPuW+USuzJycGmPn3qvC4YaBvAQEtERNS+PX/jBqLT0vBDQACmeni0yD4Hnj6Nc0VFmOftjc6Wllji59foPwZul5Vh9e3beL5zZ3SvY1i5ugKtoa/kE0pL0cnCAuvT0/F4p07wrArmTaEWAv+pGgFknIsLLBo5kUlbYaBtAAMtERFR+yaEQHp5ubYHtiWUqdVIKCtDgK1ti+1TV2xhIS4UF+MJd3eklZfjtVu38HKXLhjq6Ngq72fqGGgbwEBLREREZNqaktdMq2+ZiIiIiKiJGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNLMjd0AYxBCALg7RzARERERmZ7qnFad2+rTIQNtYWEhAMDHx8fILSEiIiKi+hQWFsLR0bHedWSiMbG3ndFoNEhLS4O9vT1kMpnBdQoKCuDj44OUlBQ4ODi0cQtNF8+LYTwvdeO5MYznxTCel7rx3BjG82JYezgvQggUFhbC29sbcnn9VbIdsodWLpejS5cujVrXwcFBshdCa+J5MYznpW48N4bxvBjG81I3nhvDeF4Mk/p5aahnthpvCiMiIiIiSWOgJSIiIiJJY6Ctg0KhwNKlS6FQKIzdFJPC82IYz0vdeG4M43kxjOelbjw3hvG8GNbRzkuHvCmMiIiIiNoP9tASERERkaQx0BIRERGRpDHQEhEREZGkMdASERERkaQx0BoQHR0Nf39/WFlZISQkBIcPHzZ2k1rMihUrMHjwYNjb28Pd3R2TJk3C9evX9daZOXMmZDKZ3mPo0KF666hUKrzwwgtwc3ODra0tHnzwQdy+fVtvndzcXEyfPh2Ojo5wdHTE9OnTkZeX19qH2GzvvPNOreP29PTUvi6EwDvvvANvb29YW1tj5MiRuHz5st4+2uN56dq1a63zIpPJ8PzzzwPoONfLoUOH8MADD8Db2xsymQw7duzQe70tr4/k5GQ88MADsLW1hZubG1588UWUl5e3xmE3Sn3npqKiAm+88QaCg4Nha2sLb29vPPXUU0hLS9Pbx8iRI2tdR1OmTNFbR2rnpqFrpi1/dqR0Xgz9eyOTyfDBBx9o12mP10tjfj935H9nGiRIz48//igsLCzEV199Ja5cuSJeeuklYWtrK5KSkozdtBYxbtw4sXHjRnHp0iVx7tw5MWHCBOHr6yuKioq068yYMUP89a9/FUqlUvvIzs7W28+8efNE586dRUxMjDh79qwYNWqU6N+/v6isrNSu89e//lUEBQWJY8eOiWPHjomgoCAxceLENjvWplq6dKno27ev3nFnZmZqX1+5cqWwt7cX27ZtExcvXhSPP/648PLyEgUFBdp12uN5yczM1DsnMTExAoDYv3+/EKLjXC979uwRS5YsEdu2bRMAxL///W+919vq+qisrBRBQUFi1KhR4uzZsyImJkZ4e3uLBQsWtPo5qEt95yYvL0/cd999YuvWreLatWvi+PHjIiwsTISEhOjtIyoqSsyZM0fvOsrLy9NbR2rnpqFrpq1+dqR2XnTPh1KpFBs2bBAymUzcunVLu057vF4a8/u5I/870xAG2hqGDBki5s2bp7esT58+4s033zRSi1pXZmamACAOHjyoXTZjxgzx0EMP1blNXl6esLCwED/++KN2WWpqqpDL5eK3334TQghx5coVAUCcOHFCu87x48cFAHHt2rWWP5AWsHTpUtG/f3+Dr2k0GuHp6SlWrlypXVZWViYcHR3FF198IYRov+elppdeekl0795daDQaIUTHvF5q/hJuy+tjz549Qi6Xi9TUVO06W7ZsEQqFQuTn57fK8TaFoYBS059//ikA6HUUREVFiZdeeqnObaR+buoKtG3xsyO181LTQw89JEaPHq23rL1fL0LU/v3Mf2fqx5IDHeXl5Thz5gzGjh2rt3zs2LE4duyYkVrVuvLz8wEALi4uessPHDgAd3d39OrVC3PmzEFmZqb2tTNnzqCiokLvPHl7eyMoKEh7no4fPw5HR0eEhYVp1xk6dCgcHR1N+lzGxcXB29sb/v7+mDJlCuLj4wEACQkJSE9P1ztmhUKBqKgo7fG05/NSrby8HJs3b8bs2bMhk8m0yzvq9VKtLa+P48ePIygoCN7e3tp1xo0bB5VKhTNnzrTqcbaU/Px8yGQyODk56S3//vvv4ebmhr59++LVV19FYWGh9rX2em7a4mdHiuelWkZGBnbv3o2nn3661mvt/Xqp+fuZ/87Uz9zYDTAlWVlZUKvV8PDw0Fvu4eGB9PR0I7Wq9QghsGjRIgwfPhxBQUHa5ePHj8ejjz4KPz8/JCQk4O2338bo0aNx5swZKBQKpKenw9LSEs7Oznr70z1P6enpcHd3r/We7u7uJnsuw8LCsGnTJvTq1QsZGRl49913ERERgcuXL2vbbOjaSEpKAoB2e1507dixA3l5eZg5c6Z2WUe9XnS15fWRnp5e632cnZ1haWkpiXNVVlaGN998E9OmTYODg4N2+RNPPAF/f394enri0qVLWLx4Mc6fP4+YmBgA7fPctNXPjtTOi65vv/0W9vb2ePjhh/WWt/frxdDvZ/47Uz8GWgN0e56AuxdWzWXtwYIFC3DhwgUcOXJEb/njjz+u/f+goCCEhobCz88Pu3fvrvWPiq6a58nQOTPlczl+/Hjt/wcHByM8PBzdu3fHt99+q71RoznXhtTPi67169dj/Pjxen+1d9TrxZC2uj6keq4qKiowZcoUaDQaREdH6702Z84c7f8HBQWhZ8+eCA0NxdmzZzFo0CAA7e/ctOXPjpTOi64NGzbgiSeegJWVld7y9n691PX7GeC/M3VhyYEONzc3mJmZ1frrIzMzs9ZfKlL3wgsvYOfOndi/fz+6dOlS77peXl7w8/NDXFwcAMDT0xPl5eXIzc3VW0/3PHl6eiIjI6PWvu7cuSOZc2lra4vg4GDExcVpRzuo79po7+clKSkJf/zxB5555pl61+uI10tbXh+enp613ic3NxcVFRUmfa4qKirw2GOPISEhATExMXq9s4YMGjQIFhYWetdRez031VrrZ0eq5+Xw4cO4fv16g//mAO3reqnr9zP/nakfA60OS0tLhISEaL+yqBYTE4OIiAgjtaplCSGwYMECbN++Hfv27YO/v3+D22RnZyMlJQVeXl4AgJCQEFhYWOidJ6VSiUuXLmnPU3h4OPLz8/Hnn39q1zl58iTy8/Mlcy5VKhWuXr0KLy8v7VdbusdcXl6OgwcPao+nvZ+XjRs3wt3dHRMmTKh3vY54vbTl9REeHo5Lly5BqVRq1/n999+hUCgQEhLSqsfZXNVhNi4uDn/88QdcXV0b3Oby5cuoqKjQXkft9dzoaq2fHamel/Xr1yMkJAT9+/dvcN32cL009PuZ/840oI1uPpOM6mG71q9fL65cuSIWLlwobG1tRWJiorGb1iKee+454ejoKA4cOKA33ElJSYkQQojCwkLxyiuviGPHjomEhASxf/9+ER4eLjp37lxrWJAuXbqIP/74Q5w9e1aMHj3a4LAg/fr1E8ePHxfHjx8XwcHBJjUMU02vvPKKOHDggIiPjxcnTpwQEydOFPb29trPfuXKlcLR0VFs375dXLx4UUydOtXgcCnt7bwIIYRarRa+vr7ijTfe0Fveka6XwsJCERsbK2JjYwUAsXr1ahEbG6u9U7+tro/q4XTGjBkjzp49K/744w/RpUsXow6nU9+5qaioEA8++KDo0qWLOHfunN6/OyqVSgghxM2bN8WyZcvEqVOnREJCgti9e7fo06ePGDhwoKTPTX3npS1/dqR0Xqrl5+cLGxsbsXbt2lrbt9frpaHfz0J07H9nGsJAa8CaNWuEn5+fsLS0FIMGDdIb0krqABh8bNy4UQghRElJiRg7dqzo1KmTsLCwEL6+vmLGjBkiOTlZbz+lpaViwYIFwsXFRVhbW4uJEyfWWic7O1s88cQTwt7eXtjb24snnnhC5ObmttGRNl31eH4WFhbC29tbPPzww+Ly5cva1zUajVi6dKnw9PQUCoVCjBgxQly8eFFvH+3xvAghxN69ewUAcf36db3lHel62b9/v8GfnRkzZggh2vb6SEpKEhMmTBDW1tbCxcVFLFiwQJSVlbXm4dervnOTkJBQ57871WMZJycnixEjRggXFxdhaWkpunfvLl588cVaY7JK7dzUd17a+mdHKuel2rp164S1tXWtsWWFaL/XS0O/n4Xo2P/ONEQmhBCt1PlLRERERNTqWENLRERERJLGQEtEREREksZAS0RERESSxkBLRERERJLGQEtEREREksZAS0RERESSxkBLRERERJLGQEtEREREksZAS0RkAr755hvIZDKcPn0aALBnzx688847xm0UEZFEMNASEZmgPXv2YNmyZcZuBhGRJDDQEhF1ICUlJcZuAhFRi2OgJSIyMTNnzsSaNWsAADKZTPtITEwEAAghEB0djQEDBsDa2hrOzs545JFHEB8fr7efkSNHIigoCIcOHUJERARsbGwwe/ZsAMC+ffswcuRIuLq6wtraGr6+vvjb3/7GwEtEkmRu7AYQEZG+t99+G8XFxfj5559x/Phx7XIvLy8AwNy5c/HNN9/gxRdfxPvvv4+cnBwsX74cEREROH/+PDw8PLTbKJVKPPnkk3j99dfx3nvvQS6XIzExERMmTEBkZCQ2bNgAJycnpKam4rfffkN5eTlsbGza/JiJiO4FAy0RkYnp3r27NpQOHTpU77UTJ07gq6++wkcffYRFixZpl0dGRqJXr15YvXo13n//fe3ynJwc/PTTTxg9erR22bZt21BWVoYPPvgA/fv31y6fNm1aax0SEVGrYskBEZGE7Nq1CzKZDE8++SQqKyu1D09PT/Tv3x8HDhzQW9/Z2VkvzALAgAEDYGlpiWeffRbffvttrVIFIiKpYaAlIpKQjIwMCCHg4eEBCwsLvceJEyeQlZWlt351mYKu7t27448//oC7uzuef/55dO/eHd27d8enn37aVodBRNSiWHJARCQhbm5ukMlkOHz4MBQKRa3Xay6TyWQG9xMZGYnIyEio1WqcPn0an332GRYuXAgPDw9MmTKlVdpORNRa2ENLRGSCqoNpaWmp3vKJEydCCIHU1FSEhobWegQHBzfpfczMzBAWFqYdVeHs2bMtcwBERG2IPbRERCaoOpi+//77GD9+PMzMzNCvXz8MGzYMzz77LGbNmoXTp09jxIgRsLW1hVKpxJEjRxAcHIznnnuu3n1/8cUX2LdvHyZMmABfX1+UlZVhw4YNAID77ruv1Y+NiKilMdASEZmgadOm4ejRo4iOjsby5cshhEBCQgK6du2KdevWYejQoVi3bh2io6Oh0Wjg7e2NYcOGYciQIQ3ue8CAAfj999+xdOlSpKenw87ODkFBQdi5cyfGjh3bBkdHRNSyZEIIYexGEBERERE1F2toiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNIYaImIiIhI0hhoiYiIiEjSGGiJiIiISNL+P8YNpbXgP/cnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# draw loss during training\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(loss_log2, color=\"c\")\n",
        "plt.xticks(fontsize=10)\n",
        "plt.yticks(fontsize=10)\n",
        "plt.xlim(-300, len(loss_log2)+300)\n",
        "plt.xlabel(\"Iters\", fontsize=12)\n",
        "plt.ylabel(\"Loss\", fontsize=12)\n",
        "plt.title(\"Learning Curve on Training Set\", fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17f0aebd",
      "metadata": {
        "id": "17f0aebd",
        "outputId": "a1e07dee-fd8a-4525-f2c0-fb942e07df5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:24<00:00,  1.61s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Path Accuracy: 10.55%\n",
            "Optimality Ratio: 0.00%\n"
          ]
        }
      ],
      "source": [
        "print(\"Test set:\")\n",
        "# init data\n",
        "data = {\"Accuracy\":[], \"Optimal\":[]}\n",
        "# eval\n",
        "nnet.eval()\n",
        "for x, c, w, z in tqdm(loader_test):\n",
        "    # cuda\n",
        "    if next(nnet.parameters()).is_cuda:\n",
        "        x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "    # predict\n",
        "    with torch.no_grad(): # no grad\n",
        "        cp = nnet(x)\n",
        "    # to numpy\n",
        "    c = c.to(\"cpu\").detach().numpy()\n",
        "    w = w.to(\"cpu\").detach().numpy()\n",
        "    z = z.to(\"cpu\").detach().numpy()\n",
        "    cp = cp.to(\"cpu\").detach().numpy()\n",
        "    # solve\n",
        "    for i in range(cp.shape[0]):\n",
        "        # sol for pred cost\n",
        "        optmodel.setObj(cp[i])\n",
        "        wpi, _ = optmodel.solve()\n",
        "        # obj with true cost\n",
        "        zpi = np.dot(wpi, c[i])\n",
        "        # round\n",
        "        zpi = zpi.round(1)\n",
        "        zi = z[i,0].round(1)\n",
        "        # regret\n",
        "        regret = (zpi - zi).round(1)\n",
        "        # accuracy\n",
        "        data[\"Accuracy\"].append((abs(wpi - w[i]) < 0.5).mean())\n",
        "        # optimal\n",
        "        data[\"Optimal\"].append(abs(regret) < 1e-2)\n",
        "# dataframe\n",
        "df2 = pd.DataFrame.from_dict(data)\n",
        "# print\n",
        "time.sleep(1)\n",
        "print(\"Path Accuracy: {:.2f}%\".format(df2[\"Accuracy\"].mean()*100))\n",
        "print(\"Optimality Ratio: {:.2f}%\".format(df2[\"Optimal\"].mean()*100))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "753c8819",
      "metadata": {
        "id": "753c8819"
      },
      "source": [
        "### 6.3 SPO+"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15b23432",
      "metadata": {
        "id": "15b23432"
      },
      "source": [
        "SPO+ model: training with smart predict-then-optimize+ loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ad38388",
      "metadata": {
        "id": "3ad38388"
      },
      "outputs": [],
      "source": [
        "# init net\n",
        "nnet = partialResNet(k=12)\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    nnet = nnet.cuda()\n",
        "# set optimizer\n",
        "optimizer = torch.optim.Adam(nnet.parameters(), lr=lr)\n",
        "# set stopper\n",
        "#stopper = earlyStopper(patience=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a1cde23e",
      "metadata": {
        "id": "a1cde23e",
        "outputId": "b698ed44-cbc5-4071-d581-4905e8485622"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of cores: 1\n"
          ]
        }
      ],
      "source": [
        "# set loss\n",
        "spoploss = pyepo.func.SPOPlus(optmodel, processes=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58a4f2c3",
      "metadata": {
        "id": "58a4f2c3",
        "outputId": "09afba43-5692-4123-9f03-9ea1d26f1d45"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch: 49, Loss: 0.5833: 100%|███████████████████████████████████████████████████████████████████████| 50/50 [3:44:20<00:00, 269.21s/it]\n"
          ]
        }
      ],
      "source": [
        "# train\n",
        "loss_log3, regret_log3 = [], [pyepo.metric.regret(nnet, optmodel, loader_test)]\n",
        "tbar = tqdm(range(epochs))\n",
        "for epoch in tbar:\n",
        "    nnet.train()\n",
        "    for x, c, w, z in loader_train:\n",
        "        # cuda\n",
        "        if torch.cuda.is_available():\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # forward pass\n",
        "        cp = nnet(x) # predicted cost\n",
        "        loss = spoploss(cp, c, w, z).mean() # loss\n",
        "        # backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # log loss\n",
        "        loss_log3.append(loss.item())\n",
        "        tbar.set_description(\"Epoch: {:2}, Loss: {:3.4f}\".format(epoch, loss.item()))\n",
        "    # scheduled learning rate\n",
        "    if (epoch == int(epochs*0.6)) or (epoch == int(epochs*0.8)):\n",
        "        for g in optimizer.param_groups:\n",
        "            g['lr'] /= 10\n",
        "    if epoch % log_step == 0:\n",
        "        # log regret\n",
        "        regret = pyepo.metric.regret(nnet, optmodel, loader_test) # regret on test\n",
        "        regret_log3.append(regret)\n",
        "        # early stop\n",
        "        #regret = pyepo.metric.regret(nnet, optmodel, loader_val) # regret on val\n",
        "        #if stopper.stop(regret):\n",
        "        #    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a694a0c5",
      "metadata": {
        "id": "a694a0c5",
        "outputId": "e8fd9087-c405-4fdd-d7d7-5ce81b8ec401"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGICAYAAABC0iY0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABp6klEQVR4nO3deVhUZRsG8PvMwgy77IuC4o7iCopg7oahlaaWS7mWRWaFZLnnWpSZmSma5pqm1qfZouaKaImm5i4uJYoLO8KwM8v5/iBGJ0AZBAbw/l3XXDrnvDPnGY7Lzct7niOIoiiCiIiIiOgJJzF1AURERERE1QGDMRERERERGIyJiIiIiAAwGBMRERERAWAwJiIiIiICwGBMRERERASAwZiIiIiICACDMRERERERAAZjIiIiIiIADMZEVEnWrVsHQRBw8uRJU5ditO7du6N79+4mO75Op8O3336L3r17w9HREXK5HM7Oznj22Wfxyy+/QKfTmay26urGjRsQBKFMjxs3bjz28e7evYvZs2fjzJkzZX5NTEwMRowYgYYNG0KpVMLR0RHt27fHhAkToFKpjK7h6NGjmD17NtLT041+LRGVTGbqAoiIqpuIiAiTHTsvLw8DBgzA3r17MXToUCxfvhyurq5ITk7Gb7/9hhdffBFbt25F//79TVZjdeTm5obo6GiDbePHj0dGRgY2bdpUbOzjunv3LubMmYMGDRqgbdu2jxx/+vRpdO7cGd7e3vjwww/RoEEDpKSk4OzZs9iyZQsmTZoEGxsbo2o4evQo5syZg9GjR6NOnTrl+yBEZIDBmIhqNVEUkZeXB3Nz8zK/pkWLFpVY0cOFhYVhz549WL9+PUaOHGmwb+DAgXj//feRm5tbIcfKycmBhYVFhbyXqSkUCnTq1Mlgm42NDQoKCoptN4XFixdDIpHg0KFDsLa21m8fPHgw5s2bB1EUTVgdERXhUgoiMqlr165h+PDhcHZ2hkKhgLe3N5YtW2YwJi8vD++99x7atm0LW1tb2NvbIyAgAD/99FOx9xMEARMmTMCKFSvg7e0NhUKB9evX65d2REZG4s0334SjoyMcHBwwcOBA3L171+A9/ruUoujH9AsXLsSiRYvg5eUFKysrBAQE4NixY8VqWLVqFZo2bQqFQoEWLVrgu+++w+jRo9GgQYOHfi0SEhLwzTffoE+fPsVCcZEmTZqgdevWAO4vV/nv0oBDhw5BEAQcOnTI4DP5+Pjg8OHDCAwMhIWFBcaOHYsBAwagfv36JS7P8Pf3R/v27fXPRVFEREQE2rZtC3Nzc9jZ2WHw4MG4fv36Qz9Xkd9//x29evWCtbU1LCwsEBgYiJ07dxqMMeY8lYdKpcKkSZPg5eUFMzMz1K1bF6GhocjOzjYY98MPP8Df3x+2trawsLBAw4YNMXbsWACFX98OHToAAMaMGaNfojF79uxSj5uamgobGxtYWVmVuF8QBIPn+/fvR69evWBjYwMLCwt07twZBw4c0O+fPXs23n//fQCAl5eXvoYHzzkRGY/BmIhM5tKlS+jQoQMuXLiAzz//HL/++iv69euHd955B3PmzNGPy8/PR1paGiZNmoQdO3Zg8+bNeOqppzBw4EBs2LCh2Pvu2LEDy5cvx4cffog9e/agS5cu+n2vvfYa5HI5vvvuOyxYsACHDh3CK6+8UqZ6ly1bhn379mHx4sXYtGkTsrOz0bdvX2RkZOjHrFy5Eq+//jpat26N7du3Y8aMGZgzZ06ZAktkZCTUajUGDBhQpnqMFR8fj1deeQXDhw/Hrl27MH78eIwdOxZxcXE4ePCgwdjLly/jzz//xJgxY/Tb3njjDYSGhqJ3797YsWMHIiIicPHiRQQGBiIxMfGhx46KikLPnj2RkZGB1atXY/PmzbC2tsZzzz2HrVu3Fhv/OOepNDk5OejWrRvWr1+Pd955B7t378bkyZOxbt06PP/88/pZ2+joaAwZMgQNGzbEli1bsHPnTnz44YfQaDQAgPbt22Pt2rUAgBkzZiA6OhrR0dF47bXXSj12QEAA4uPj8fLLLyMqKuqhs/4bN25EUFAQbGxssH79enz//fewt7dHnz599OH4tddew9tvvw0A2L59u76GB7+RIaJyEImIKsHatWtFAOKJEydKHdOnTx+xXr16YkZGhsH2CRMmiEqlUkxLSyvxdRqNRlSr1eKrr74qtmvXzmAfANHW1rbYa4vqGT9+vMH2BQsWiADE+Ph4/bZu3bqJ3bp10z+PjY0VAYitWrUSNRqNfvuff/4pAhA3b94siqIoarVa0dXVVfT39zc4xs2bN0W5XC7Wr1+/1K+FKIriJ598IgIQf/vtt4eO++9nio2NNdgeGRkpAhAjIyMNPhMA8cCBAwZj1Wq16OLiIg4fPtxg+wcffCCamZmJKSkpoiiKYnR0tAhA/Pzzzw3G3bp1SzQ3Nxc/+OCDh9baqVMn0dnZWczMzNRv02g0oo+Pj1ivXj1Rp9MZfKaynKdH6datm9iyZUv98/DwcFEikRT7M/m///1PBCDu2rVLFEVRXLhwoQhATE9PL/W9T5w4IQIQ165dW6Za8vLyxAEDBogARACiVCoV27VrJ06fPl1MSkrSj8vOzhbt7e3F5557zuD1Wq1WbNOmjdixY0f9ts8++6zE809E5ccZYyIyiby8PBw4cAAvvPACLCwsoNFo9I++ffsiLy/PYJnCDz/8gM6dO8PKygoymQxyuRyrV69GTExMsffu2bMn7OzsSjzu888/b/C8aFnCzZs3H1lzv379IJVKS33tlStXkJCQgJdeesngdZ6enujcufMj37+y2dnZoWfPngbbZDIZXnnlFWzfvl0/863VavHtt9+if//+cHBwAAD8+uuvEAQBr7zyisG5cnV1RZs2bR46I56dnY3jx49j8ODBBksJpFIpRowYgdu3b+PKlSsGr3mc81SaX3/9FT4+Pmjbtq3BZ+jTp4/BMoSiZRIvvfQSvv/+e9y5c6fcxyyiUCjw448/4tKlS/jiiy8wdOhQJCcn46OPPoK3t7f+8x89ehRpaWkYNWqUQY06nQ7PPPMMTpw4UWzZBxFVHAZjIjKJ1NRUaDQafPXVV5DL5QaPvn37AgBSUlIAFP6o+KWXXkLdunWxceNGREdH48SJExg7dizy8vKKvffDug4UBb0iCoUCAMp0QdujXpuamgoAcHFxKfbakrb9l6enJwAgNjb2kWPLo7SvS9HXccuWLQCAPXv2ID4+3mAZRWJiIkRRhIuLS7HzdezYMf25Ksm9e/cgimKJx3d3dwdw/2tX5HHOU2kSExNx7ty5YvVbW1tDFEX9Z+jatSt27NgBjUaDkSNHol69evDx8cHmzZvLfewi3t7eCA0NxcaNGxEXF4dFixYhNTUVM2fO1NcIFF6U9986P/30U4iiiLS0tMeug4hKxq4URGQSdnZ2+hnDt956q8QxXl5eAArXXHp5eWHr1q0GFynl5+eX+Lr/XshUVYrCXEnrbRMSEh75+h49ekAul2PHjh0ICQl55HilUgmg+NehtJBa2telRYsW6NixI9auXYs33ngDa9euhbu7O4KCgvRjHB0dIQgCjhw5og+pDyppWxE7OztIJBLEx8cX21d0QZ2jo2Opr68ojo6OMDc3x5o1a0rdX6R///7o378/8vPzcezYMYSHh2P48OFo0KABAgICKqQeQRAwceJEzJ07FxcuXDCo4auvviq1m0ZZvskiovJhMCYik7CwsECPHj1w+vRptG7dGmZmZqWOFQQBZmZmBsEuISGhxK4UptSsWTO4urri+++/R1hYmH57XFwcjh49qp8dLY2rqytee+01LF++HBs2bCixM8U///yD7OxstG7dWt/l4ty5c2jWrJl+zM8//2x07WPGjMGbb76J33//Hb/88gvCwsIMlo08++yz+OSTT3Dnzp1iS0UexdLSEv7+/ti+fTsWLlyob52n0+mwceNG1KtXD02bNjW6ZmM9++yz+Pjjj+Hg4KD/putRFAoFunXrhjp16mDPnj04ffo0AgICjJ7Bjo+PL3HG/O7du1CpVPD19QUAdO7cGXXq1MGlS5cwYcKER9ZmTA1E9GgMxkRUqQ4ePFjincb69u2LL7/8Ek899RS6dOmCN998Ew0aNEBmZib+/vtv/PLLL/pOCc8++yy2b9+O8ePHY/Dgwbh16xbmzZsHNzc3XLt2rYo/UekkEgnmzJmDN954A4MHD8bYsWORnp6OOXPmwM3NDRLJo1evLVq0CNevX8fo0aOxZ88evPDCC3BxcUFKSgr27duHtWvXYsuWLWjdujU6dOiAZs2aYdKkSdBoNLCzs8OPP/6I33//3ejahw0bhrCwMAwbNgz5+fkYPXq0wf7OnTvj9ddfx5gxY3Dy5El07doVlpaWiI+Px++//45WrVrhzTffLPX9w8PD8fTTT6NHjx6YNGkSzMzMEBERgQsXLmDz5s1VMssfGhqKbdu2oWvXrpg4cSJat24NnU6HuLg47N27F++99x78/f3x4Ycf4vbt2+jVqxfq1auH9PR0fPnll5DL5ejWrRsAoFGjRjA3N8emTZvg7e0NKysruLu7l/rNz+uvv4709HQMGjQIPj4+kEqluHz5Mr744gtIJBJMnjwZAGBlZYWvvvoKo0aNQlpaGgYPHgxnZ2ckJyfj7NmzSE5OxvLlywEArVq1AgB8+eWXGDVqFORyOZo1a2bQJ5mIjGTaa/+IqLYq6i5Q2qPoSvrY2Fhx7NixYt26dUW5XC46OTmJgYGB4vz58w3e75NPPhEbNGggKhQK0dvbW1y1apU4a9Ys8b//jAEQ33rrrVLr+W9HgtI6OJTUleKzzz4r9r4AxFmzZhlsW7lypdi4cWPRzMxMbNq0qbhmzRqxf//+xTpolEaj0Yjr168Xe/bsKdrb24symUx0cnISg4ODxe+++07UarX6sVevXhWDgoJEGxsb0cnJSXz77bfFnTt3lviZHuzQUJLhw4eLAMTOnTuXOmbNmjWiv7+/aGlpKZqbm4uNGjUSR44cKZ48efKRn+vIkSNiz5499a/t1KmT+MsvvxiMMeY8PUpJnzkrK0ucMWOG2KxZM9HMzEy0tbUVW7VqJU6cOFFMSEgQRVEUf/31VzE4OFisW7euaGZmJjo7O4t9+/YVjxw5YvBemzdvFps3by7K5fIS/xw8aM+ePeLYsWPFFi1aiLa2tqJMJhPd3NzEgQMHitHR0cXGR0VFif369RPt7e1FuVwu1q1bV+zXr5/4ww8/GIybOnWq6O7uLkokEqO/PkRUnCCKvN0OEVFlSk9PR9OmTTFgwACsXLnS1OUQEVEpuJSCiKgCJSQk4KOPPkKPHj3g4OCAmzdv4osvvkBmZibeffddU5dHREQPwWBMRFSBFAoFbty4gfHjxyMtLQ0WFhbo1KkTVqxYgZYtW5q6PCIieggupSAiIiIiAm/wQUREREQEgMGYiIiIiAgAgzEREREREQBefPdYdDod7t69C2tra5PdgpaIiIiISieKIjIzM+Hu7v7IGy0xGD+Gu3fvwsPDw9RlEBEREdEj3Lp1C/Xq1XvoGAbjx1B0281bt27BxsamQt/7z4u5mLs6BU09zbAo1KVC35uIiIjoSaFSqeDh4VGm26UzGD+GouUTNjY2FR6MLa3kkJnlw0xpVuHvTURERPSkKcuyV158V82xyzQRERFR1WAwrqZ4KR8RERFR1WIwJiIiIiICg3H1x6UURERERFWCwbi64loKIiIioirFYExEREREBAbjao8rKYiIiIiqBoNxNcU7TBMRERFVLQZjIiIiIiIwGFd7Iu/wQURERFQlGIyrKa6kICIiIqpaDMZERERERGAwrva4kIKIiIioajAYV1PsSkFERERUtRiMiYiIiIjAYFz9cS0FERERUZVgMCYiIiIiAoMxEREREREABuNqjyspiIiIiKoGg3E1xa4URERERFWLwZiIiIiICAzG1Z7ItRREREREVYLBuJoSuJaCiIiIqEoxGBMRERERgcG42iqaL9bqTFoGERER0RODwbiacqwjBQAkpmmg03GhMREREVFlYzCuptydZJBKgLx8EcnpWlOXQ0RERFTrMRhXUzKpgHrOMgBAXILaxNUQERER1X4MxtWYp6scAHCTwZiIiIio0jEYV2NFwTguQWPiSoiIiIhqPwbjaqx+0YxxPGeMiYiIiCobg3E1xqUURERERFWHwbga83ApvPhOla1DeiY7UxARERFVpmoRjCMiIuDl5QWlUglfX18cOXLkoeOjoqLg6+sLpVKJhg0bYsWKFQb7t2/fDj8/P9SpUweWlpZo27Ytvv3228c+blUzV0jgYl/Yz5izxkRERESVy+TBeOvWrQgNDcX06dNx+vRpdOnSBcHBwYiLiytxfGxsLPr27YsuXbrg9OnTmDZtGt555x1s27ZNP8be3h7Tp09HdHQ0zp07hzFjxmDMmDHYs2dPuY9rKvX1F+AxGBMRERFVJkEURZPeVs3f3x/t27fH8uXL9du8vb0xYMAAhIeHFxs/efJk/Pzzz4iJidFvCwkJwdmzZxEdHV3qcdq3b49+/fph3rx55TpuSVQqFWxtbZGRkQEbG5syvcZYy7fdww8HMjGwhzUmvGhXKccgIiIiqq2MyWsmnTEuKCjAqVOnEBQUZLA9KCgIR48eLfE10dHRxcb36dMHJ0+ehFpdfFZVFEUcOHAAV65cQdeuXct9XADIz8+HSqUyeFQ2T84YExEREVUJkwbjlJQUaLVauLi4GGx3cXFBQkJCia9JSEgocbxGo0FKSop+W0ZGBqysrGBmZoZ+/frhq6++wtNPP13u4wJAeHg4bG1t9Q8PDw+jPm95cCkFERERUdUw+RpjABAEweC5KIrFtj1q/H+3W1tb48yZMzhx4gQ++ugjhIWF4dChQ4913KlTpyIjI0P/uHXr1kM/V0XwdC3sTJF0T4vcPF2lH4+IiIjoSSUz5cEdHR0hlUqLzdImJSUVm80t4urqWuJ4mUwGBwcH/TaJRILGjRsDANq2bYuYmBiEh4eje/fu5TouACgUCigUCqM+4+OysZTCzlqCe5k6xCWq0ax+1R6fiIiI6Elh0hljMzMz+Pr6Yt++fQbb9+3bh8DAwBJfExAQUGz83r174efnB7lcXuqxRFFEfn5+uY9rSvdv9MFbQxMRERFVFpPOGANAWFgYRowYAT8/PwQEBGDlypWIi4tDSEgIgMLlC3fu3MGGDRsAFHagWLp0KcLCwjBu3DhER0dj9erV2Lx5s/49w8PD4efnh0aNGqGgoAC7du3Chg0bDDpQPOq41Ymnqxxnr+VznTERERFRJTJ5MB4yZAhSU1Mxd+5cxMfHw8fHB7t27UL9+vUBAPHx8Qa9hb28vLBr1y5MnDgRy5Ytg7u7O5YsWYJBgwbpx2RnZ2P8+PG4ffs2zM3N0bx5c2zcuBFDhgwp83Grk/q8NTQRERFRpTN5H+OarCr6GAPAyZhcfPBVMjxcZFg/y73SjkNERERU29SYPsZUNkUzxneSNVBr+H0MERERUWVgMK4BHOtIYaEUoNMVhmMiIiIiqngMxjWAIAjwcOGNPoiIiIgqE4NxDcEL8IiIiIgqF4NxDVF0BzzOGBMRERFVDgbjGoIzxkRERESVi8G4hii6+92tBA10OnamICIiIqpoDMY1hLujDHIZkK8WkXRPa+pyiIiIiGodBuMaQioVUNeJyymIiIiIKguDcQ3CC/CIiIiIKg+DcQ3CC/CIiIiIKg+DcQ1SdAFeXALvfkdERERU0RiMa5D6rvfvfieK7ExBREREVJEYjGsQDxcZBAFQZeuQnqUzdTlEREREtQqDcQ2iMJPA1V4KAIiL5zpjIiIioorEYFzDePICPCIiIqJKwWBcw3g+sM6YiIiIiCoOg3ENc79lGztTEBEREVUkBuMahjPGRERERJWDwbiGqe9WGIyT07XIyWNnCiIiIqKKwmBcw1hbSGBnU3jaOGtMREREVHEYjGug+i5cTkFERERU0RiMa6Ci5RS8AI+IiIio4jAY10C8AI+IiIio4jEY10D1eZMPIiIiogrHYFwDebrKAAB3UzRQa0QTV0NERERUOzAY10AOtlJYKgXodMDtJM4aExEREVUEBuMaSBCEB9YZ8wI8IiIioorAYFxD8QI8IiIioorFYFxDFV2Ad4PBmIiIiKhCMBjXUEUX4HHGmIiIiKhiGB2Me/bsicuXL5e47+rVq+jZs+djF0WPVjRjfCtRA62OnSmIiIiIHpfRwfjQoUNQqVQl7svMzERUVNRjF0WP5uoog1wGFKhFJKZpTV0OERERUY1XoUsp4uPjYWFhYfTrIiIi4OXlBaVSCV9fXxw5cuSh46OiouDr6wulUomGDRtixYoVBvtXrVqFLl26wM7ODnZ2dujduzf+/PNPgzGzZ8+GIAgGD1dXV6NrNxWpRICH878X4MVzOQURERHR45KVZdBPP/2En376Sf983rx5cHJyMhiTm5uLQ4cOoV27dkYVsHXrVoSGhiIiIgKdO3fG119/jeDgYFy6dAmenp7FxsfGxqJv374YN24cNm7ciD/++APjx4+Hk5MTBg0aBKBwVnvYsGEIDAyEUqnEggULEBQUhIsXL6Ju3br692rZsiX279+vfy6VSo2q3dQ8XeW4fleNmwlqdGplbupyiIiIiGq0MgXjS5cu4YcffgBQ2EP34MGDkEgMJ5sVCgVatWqFL7/80qgCFi1ahFdffRWvvfYaAGDx4sXYs2cPli9fjvDw8GLjV6xYAU9PTyxevBgA4O3tjZMnT2LhwoX6YLxp0yaD16xatQr/+9//cODAAYwcOVK/XSaT1ahZ4v/iBXhEREREFadMSymmTp2KzMxMZGZmQhRFREZG6p8XPVJSUhAZGYnWrVuX+eAFBQU4deoUgoKCDLYHBQXh6NGjJb4mOjq62Pg+ffrg5MmTUKtLDog5OTlQq9Wwt7c32H7t2jW4u7vDy8sLQ4cOxfXr1x9ab35+PlQqlcHDlIouwLvJYExERET02IxeY6zT6dCxY8cKOXhKSgq0Wi1cXFwMtru4uCAhIaHE1yQkJJQ4XqPRICUlpcTXTJkyBXXr1kXv3r312/z9/bFhwwbs2bMHq1atQkJCAgIDA5GamlpqveHh4bC1tdU/PDw8yvpRK8WDN/kQRXamICIiInoc5b74bs+ePZg6dSrGjRuHuLg4AMCJEyeQnJxs9HsJgmDwXBTFYtseNb6k7QCwYMECbN68Gdu3b4dSqdRvDw4OxqBBg9CqVSv07t0bO3fuBACsX7++1ONOnToVGRkZ+setW7ce/eEqkYeLHBIByMoVcU+lM2ktRERERDVdmdYYPygnJwf9+/fHgQMH9EH0zTffhKenJxYuXAgPDw8sXLiwTO/l6OgIqVRabHY4KSmp2KxwEVdX1xLHy2QyODg4GGxfuHAhPv74Y+zfv/+RSzwsLS3RqlUrXLt2rdQxCoUCCoXioe9TlczkAlwdZbibrMHNBDXsbWvWxYNERERE1YnRM8bTp0/HyZMnsW3bNmRkZBj8CD8oKMigy8OjmJmZwdfXF/v27TPYvm/fPgQGBpb4moCAgGLj9+7dCz8/P8jlcv22zz77DPPmzcNvv/0GPz+/R9aSn5+PmJgYuLm5lbn+6sDThRfgEREREVUEo4PxDz/8gHnz5uGFF16AublhizBPT0/9soqyCgsLwzfffIM1a9YgJiYGEydORFxcHEJCQgAULl94sJNESEgIbt68ibCwMMTExGDNmjVYvXo1Jk2apB+zYMECzJgxA2vWrEGDBg2QkJCAhIQEZGVl6cdMmjQJUVFRiI2NxfHjxzF48GCoVCqMGjXK2C+JSfECPCIiIqKKYfRSiuTkZLRs2bLEfRKJBLm5uUa935AhQ5Camoq5c+ciPj4ePj4+2LVrF+rXrw+g8KYhD4ZtLy8v7Nq1CxMnTsSyZcvg7u6OJUuW6Fu1AYU3DCkoKMDgwYMNjjVr1izMnj0bAHD79m0MGzYMKSkpcHJyQqdOnXDs2DH9cWsKT7f7F+ARERERUfkZHYzr1q2L8+fPo0ePHsX2nTt3Dl5eXkYXMX78eIwfP77EfevWrSu2rVu3bvjrr79Kfb8bN2488phbtmwpa3nV2v0ZY42JKyEiIiKq2YxeSjFw4EB89NFHOH36tH6bIAi4efMmvvjiC7z44osVWiA9XFHLttQMLbJy2ZmCiIiIqLyMDsazZs2Cu7s7OnbsCD8/PwiCgDFjxsDHxwfOzs6YMmVKZdRJpbAyl8Dh324Ut7icgoiIiKjcjA7G1tbWOHr0KObNmwcrKys0atQIFhYWmDp1Kg4fPlzsgjyqfEW3huYFeERERETlZ9Qa49zcXLz66qsYP348pkyZwtnhasLTVY7TV/J5AR4RERHRYzBqxtjc3Bw//fQTdDquZa1OeAEeERER0eMzeilF27ZtceHChcqohcqp6AI8zhgTERERlZ/RwfiTTz7BggULEBUVVRn1UDkUzRjHp2hQoBYfMZqIiIiISmJ0H+Px48cjKysLPXv2hJ2dHdzc3CAIgn6/IAg4e/ZshRZJD2dvI4GluYDsXBG3k9RoWNfM1CURERER1ThGB2MHBwc4OjpWRi1UToIgoL6rHJdiCxCXwGBMREREVB5GB+NDhw5VQhn0uIqCMS/AIyIiIiofo9cYU/VUdAHezXhegEdERERUHkbPGB8+fLjUfRKJBHXq1EHz5s0hkxn91vQYmtUvXD5x/GIuMnN0sLbg9zxERERExjA6vXbv3t3gYruSWFlZISwsDLNmzSp3YWScNk0UaFhXjut31NgRlYkRwbamLomIiIioRjF6WvGXX35B/fr1ERQUhLVr12LXrl1Ys2YNnn76aXh6emLdunUYMmQI5s2bh6+++qoyaqYSCIKAYUE2AIDtkZnIK+BNWIiIiIiMIYiiaFTj27fffhuZmZlYt25dsX2jRo2CUqnE119/jddffx3Hjh3DuXPnKqrWakelUsHW1hYZGRmwsbExdTnQakWMnBOP+BQNJrxoh4E9rE1dEhEREZFJGZPXjJ4x3rp1K4YNG1bivuHDh2P79u0AgGeffRbXrl0z9u3pMUilAoY+XRiGv9+vgkbLm30QERERlZXRwTg7OxvJyckl7ktMTEROTg4AwNramhfgmUCfTlawt5Eg6Z4WB05km7ocIiIiohrD6GDcuXNnzJw5E1euXDHYfvnyZXz44Yd46qmnAADXr19HvXr1KqZKKjMzuYDBPQt/TLBlrwo6HWeNiYiIiMrC6GC8ePFiZGZmomXLlmjbti369OmDtm3bwsfHB9nZ2Vi8eDEA4O7duxg1alRF10tl8FwXK1iaC7iZoMHRc7mmLoeIiIioRjD64jsASEhIwKJFi3D48GGkpqbCwcEB3bp1Q2hoKNzc3Cqjzmqpul1896DVP6Vj0x4Vmjcww7L3XR7ZYo+IiIioNjImr5UrGFOh6hyM72VqMWzGXRSoRSx81xntmylNXRIRERFRlavUrhRFMjIysGfPHmzatAn37t0r79tQJbGzliI40BIAsHmPysTVEBEREVV/5QrG8+bNg7u7O4KDgzFy5EjExsYCAHr16oVPPvmkQguk8hvS2wYSCXDqch6uxhWYuhwiIiKias3oYBwREYE5c+bg1Vdfxc6dO/HgSoxnn30WO3furNACqfxcHWTo5WcBAPhuT4aJqyEiIiKq3owOxkuXLkVYWBiWLFmCoKAgg31NmjThTT2qmaH/3ib6yJlcxCWqTVwNERERUfVldDC+fv06+vTpU+I+a2trpKenP25NVIG83M0Q2Nocoghs3cu1xkRERESlMToY29raIjExscR9N27cgLOz82MXRRVreJ/CWeN9f2Yj+Z7GxNUQERERVU9GB+NevXphwYIFyM6+f7thQRCg0WiwfPnyUmeTyXRaeCnQpokCGi3ww4FMU5dDREREVC0ZHYznzp2LmzdvokWLFnjvvfcgCAKWLl2Kjh074u+//8bMmTMro056TEWzxr/+kYWMLK2JqyEiIiKqfowOxo0bN8Yff/wBb29vREREQBRFbNiwAY6Ojjhy5Ag8PT0ro056TH7eSjT2kCMvX8SPhzhrTERERPRf5epj3KJFC/z222/IzMzE7du3oVKpsHfvXnh7eyMzk6GrOhIEAcP/7VCxIyoLuXk6E1dEREREVL2U+853AKBQKODu7g5zc3NkZ2fj448/hpeXV0XVRhWsSzsL1HWSQZWtw69/ZJm6HCIiIqJqpczB+J9//sGcOXMwfvx4fPnll1CpClt/qdVqLFq0CF5eXpgxYwaaNGlSacXS45FKBAx5unDW+IcDmShQi494BREREdGTQ1aWQadOnUL37t0NOlGsXr0av/76KwYMGIAzZ86gcePGiIiIwODBgyutWHp8Qf6WWL8zAynpWhw4kY3gQCtTl0RERERULZRpxnjOnDlQKBRYv349Ll68iF9++QVqtRqBgYE4d+4c5s+fj0uXLpU7FEdERMDLywtKpRK+vr44cuTIQ8dHRUXB19cXSqUSDRs2xIoVKwz2r1q1Cl26dIGdnR3s7OzQu3dv/Pnnn4993NrATC7gxV7WAIDNe1XQ6jhrTERERASUMRgfO3YMs2fPxogRI+Dt7Y1+/fph9erVuHv3LqZNm4Zp06ZBJivT5HMxW7duRWhoKKZPn47Tp0+jS5cuCA4ORlxcXInjY2Nj0bdvX3Tp0gWnT5/GtGnT8M4772Dbtm36MYcOHcKwYcMQGRmJ6OhoeHp6IigoCHfu3Cn3cWuTZ5+ygrWFBLeTNPj9TK6pyyEiIiKqFgRRFB85ZSiTyRAVFYXOnTvrt+Xl5cHCwgKHDx/GU089Ve4C/P390b59eyxfvly/zdvbGwMGDEB4eHix8ZMnT8bPP/+MmJgY/baQkBCcPXsW0dHRJR5Dq9XCzs4OS5cuxciRI8t13JKoVCrY2toiIyMDNjY2ZXpNdbH2l3R8u1sF3+ZKfPYO71ZIREREtZMxea1MM8Y6nQ5yudxgW9FzCwuLcpYJFBQU4NSpUwgKCjLYHhQUhKNHj5b4mujo6GLj+/Tpg5MnT0KtVpf4mpycHKjVatjb25f7uACQn58PlUpl8KipenWwBACc+zsPufls3UZERERU5vUPhw4dwu3bt/XPdTodBEFAZGQkbty4YTB24MCBZXrPlJQUaLVauLi4GGx3cXFBQkJCia9JSEgocbxGo0FKSgrc3NyKvWbKlCmoW7cuevfuXe7jAkB4eDjmzJlTps9W3Xm4yODqIEVCqhZnruYjoJW5qUsiIiIiMqkyB+MpU6aUuP399983eC4IArRa4245LAiCwXNRFItte9T4krYDwIIFC7B582YcOnQISqXysY47depUhIWF6Z+rVCp4eHiUOr46EwQBHVuY4+cjWTh+IZfBmIiIiJ54ZQrGkZGRlXJwR0dHSKXSYrO0SUlJxWZzi7i6upY4XiaTwcHBwWD7woUL8fHHH2P//v1o3br1Yx0XKLyhiUKhKNNnqwk6tlTi5yNZ+PNS7iO/KSAiIiKq7coUjLt161YpBzczM4Ovry/27duHF154Qb9937596N+/f4mvCQgIwC+//GKwbe/evfDz8zNYB/3ZZ59h/vz52LNnD/z8/B77uLVRu2ZKyGVAQqoWtxI18HSVP/pFRERERLXUY90SuiKEhYXhm2++wZo1axATE4OJEyciLi4OISEhAAqXLxR1kgAKO1DcvHkTYWFhiImJwZo1a7B69WpMmjRJP2bBggWYMWMG1qxZgwYNGiAhIQEJCQnIysoq83GfBOYKCVo3Llxecvwi27YRERHRk618zYcr0JAhQ5Camoq5c+ciPj4ePj4+2LVrF+rXrw8AiI+PN+gt7OXlhV27dmHixIlYtmwZ3N3dsWTJEgwaNEg/JiIiAgUFBcVuODJr1izMnj27TMd9UnRsqcSpy3n482IeXuxVs1rOEREREVWkMvUxppLV5D7GRW7GqzFmXjzkMmDHZ/VgrjD5DxGIiIiIKkyF9zGm2svTVQYXeynUGuDM1XxTl0NERERkMgzGTzhBENCxZWGrtj+5zpiIiIieYAzGhI4tCy/A+/NiLriyhoiIiJ5UZbr4bsOGDUa96YNdJKj6a9+0sG1bfKoWt5I08HRh2zYiIiJ68pQpGI8ePdrgedGNIB6cXXzw5hAMxjWLuVKCVo0U+OtKPv68mMtgTERERE+kMi2liI2N1T+io6Ph4eGB1157DZGRkYiJiUFkZCReffVVeHh44OjRo5VdM1WC++uM80xcCREREZFpGN2ubdiwYXB1dcUXX3xRbN/EiRNx9+5dbN26tcIKrM5qQ7u2Ijfi1Rj7b9u2nxbWg9KMy8+JiIio5qvUdm27d+9Gv379StzXt29f7Nmzx9i3pGqgvqsMzkVt266wbRsRERE9eYwOxjqdDteuXStx37Vr19jVoIYSBAH+/y6nOH6JbduIiIjoyWN0MH7mmWcwffp07Ny502D7r7/+ihkzZqBPnz4VVhxVrftt2/L4DQ4RERE9ccrUleJBX375JXr16oXnn38e1tbWcHFxQWJiIjIzM9GkSRN8+eWXlVEnVQF927YUDW4naeDB7hRERET0BDE6GLu5ueGvv/7CunXrcOjQIaSmpqJdu3bo0aMHRo4cCXNz88qok6rAf9u2MRgTERHRk8TorhR0X23qSlHk+/0qrNiejg4tlPh0grOpyyEiIiJ6LJXalaLI5cuX8fXXX+Ojjz5CQkICAODu3bvIzeWFWzVZUT/jM1fzkFegM3E1RERERFXH6KUUWq0Wr7/+OtatWwdRFCEIAoKDg+Hq6oo33ngD7dq1w9y5cyujVqoCRW3bktK0OHM1H518uDSGiIiIngxGzxh/9NFH+O677/DZZ5/hwoULBt0LgoOD8dtvv1VogVS1BEGAf4uiu+Bx9p+IiIieHEYH43Xr1mHmzJkICwtDs2bNDPZ5eXkhNja2wooj0yhq23acbduIiIjoCWJ0ML5z5w4CAgJK3KdUKpGZmfnYRZFptWumhEx6v20bERER0ZPA6GDs7OyM69evl7jvypUrqFev3mMXRaZloZSgVWMFAC6nICIioieH0cG4b9+++Oijj3Dnzh39NkEQkJGRgSVLluC5556r0ALJNDoWrTO+lGfiSoiIiIiqhtHBeO7cudBoNGjRogUGDRoEQRAwbdo0+Pj4IC8vDzNnzqyMOqmKFa0zZts2IiIielIYHYxdXFxw4sQJDBs2DKdOnYJUKsXZs2cRHByMo0ePwt7evjLqpCrWwE0OZzsp1BrgzNV8U5dDREREVOmM7mMMFIbjFStWVHQtVI0IgoCOLc3x6+9Z+PNiLvsZExERUa1n9Izxhg0bcPz48RL3paSkYMOGDY9dFFUPHVsULqfgOmMiIiJ6EhgdjEePHo0uXbpgzZo1xfb9888/GDNmTIUURqbXvnlh27a7yRrcTlKbuhwiIiKiSmV0MAaA7t27Y9y4cZg9e3YFl0PViYVSAp9GRW3bOGtMREREtVu5gvH8+fPx6aefYt68eXj11Veh1Worui6qJjq25O2hiYiI6MlQrmAMAJMmTcLGjRuxadMmPPvss8jOzq7Iuqia8C9q23YtH/ls20ZERES1WLmDMQAMGzYMu3fvRnR0NLp164aEhISKqouqiQZucjjVkaJALeLMNbZtIyIiotrrsYIxAPTo0QOHDx9GQkICXnnllYqoiaqRwrZt/3an4HIKIiIiqsWMDsbdunWDjY2NwbbWrVvj6NGj8PT0rLDCqPq4v86YF+ARERFR7WX0DT4iIyNL3O7p6YmLFy8+dkFU/bRvVti27U6yBneS1KjrLDd1SUREREQV7rGXUlDtZ2l+v23bku/v4fv9Khw9l4O4RDXUGtHE1RERERFVjDLNGI8dOxYzZ86El5cXxo4d+9CxgiBg9erVFVIcVR9PtbHAmav5OHEpDyceuBOeRAK4Ocjg4SJDPWc56jnLUM9FDg9nGRzrSCEIggmrJiIiIiq7MgXjyMhIvPvuuwCAgwcPPjTslCcIRURE4LPPPkN8fDxatmyJxYsXo0uXLqWOj4qKQlhYGC5evAh3d3d88MEHCAkJ0e+/ePEiPvzwQ5w6dQo3b97EF198gdDQUIP3mD17NubMmWOwzcXFhZ01SvF8VyvY20oRe6cAt5M0uJWkxu0kDfLyxcIlFskaAIZrkG0sJfhghD0CW1uYpmgiIiIiI5QpGMfGxup/f+PGjQotYOvWrQgNDUVERAQ6d+6Mr7/+GsHBwbh06VKJF/PFxsaib9++GDduHDZu3Ig//vgD48ePh5OTEwYNGgQAyMnJQcOGDfHiiy9i4sSJpR67ZcuW2L9/v/65VCqt0M9Wm8ikArq3t0D39vdDriiKSMnQ4nZi4S2jbydpcCux8Ne7KRqosnVY8G0a1n2oQB1rfm2JiIioehNEUTTpIlF/f3+0b98ey5cv12/z9vbGgAEDEB4eXmz85MmT8fPPPyMmJka/LSQkBGfPnkV0dHSx8Q0aNEBoaGiJM8Y7duzAmTNnyl27SqWCra0tMjIyinXqeNIVqEWMX5CA63fU6N3BAtPGOJq6JCIiInoCGZPXTHrxXUFBAU6dOoWgoCCD7UFBQTh69GiJr4mOji42vk+fPjh58iTUarVRx7927Rrc3d3h5eWFoUOH4vr16w8dn5+fD5VKZfCgkpnJBUx62R4SAdh/IgfH2QOZiIiIqrkyLaXw8vIq89phQRDwzz//lGlsSkoKtFotXFxcDLY/bK1vQkJCieM1Gg1SUlLg5uZWpmP7+/tjw4YNaNq0KRITEzF//nwEBgbi4sWLcHBwKPE14eHhxdYlU+maN1BgYA9r/O9gJhZvTsOaGW4wV7IRChEREVVPZQrG3bp1q9TuAv99b1EUjbrAr2g1iDE1BgcH63/fqlUrBAQEoFGjRli/fj3CwsJKfM3UqVMN9qlUKnh4eJT5mE+iMc/a4siZHCSmabHm1wy8NdjO1CURERERlahMwXjdunWVcnBHR0dIpdJis8NJSUnFZoWLuLq6ljheJpOVOtNbFpaWlmjVqhWuXbtW6hiFQgGFQlHuYzyJzJUSTBxmjynLkvFjZCZ6+lnAuwG/hkRERFT9mPTn2mZmZvD19cW+ffsMtu/btw+BgYElviYgIKDY+L1798LPzw9yefnvyJafn4+YmJgyL8WgsuvY0hy9O1pAJwKfb0yDRsubghAREVH1Y/QtoYtkZGTg6tWryM0tflFV165dy/w+YWFhGDFiBPz8/BAQEICVK1ciLi5O35d46tSpuHPnDjZs2ACgsAPF0qVLERYWhnHjxiE6OhqrV6/G5s2b9e9ZUFCAS5cu6X9/584dnDlzBlZWVmjcuDEAYNKkSXjuuefg6emJpKQkzJ8/HyqVCqNGjSrvl4QeYvwgO/x5MQ/X76qxdZ8KLz9ja+qSiIiIiAwYHYw1Gg1CQkKwYcMGaLXaEseUtr0kQ4YMQWpqKubOnYv4+Hj4+Phg165dqF+/PgAgPj4ecXFx+vFeXl7YtWsXJk6ciGXLlsHd3R1LlizR9zAGgLt376Jdu3b65wsXLsTChQvRrVs3HDp0CABw+/ZtDBs2DCkpKXByckKnTp1w7Ngx/XGpYtWxluKtwXYIX5+KDbsy0LWdBTxcyj/DT0RERFTRjO5j/Nlnn2HhwoX4/PPPMXLkSCxbtgxyuRyrVq1CRkYGlixZUqydWm3FPsbGEUURU5Yl48SlPLRposDn7zpDIuEto4mIiKjyVGof42+//RbTp0/HsGHDABS2PXvttddw/Phx1K9fH5GRkeWrmmo9QRAQOtQeSjMBZ6/lY3d0tqlLIiIiItIzOhhfv34dbdq0gURS+NK8vDz9vpCQEGzatKniqqNax81RhjHPFa4vXrH9HlIzyr7shoiIiKgyGR2MLS0tUVBQAEEQYG9vj5s3b+r3mZubIzU1tUILpNpnYA9rNPM0Q3auiK++TzN1OUREREQAyhGMmzdvjtjYWABAYGAgFi1ahNu3byMpKQkLFixAs2bNKrxIql2kEgGTXrGHRAIcPp2L38/kGP0eGq2IlHRNJVRHRERETyqju1IMGTIEV69eBQDMmTMHXbt21XdykMvl2L59e8VWSLVSo3pmGNLbBpv3qvDl1nto20wJK/OHf5+m1og4dTkPR07n4I9zuVBl6/BSb2uMG1AHUl7ER0RERI/J6K4U/3Xr1i3s2LEDgiDg6aeffqJmjNmV4vHkF+jw2kcJuJOswfNdrBA6zL7EMSdj8hB1OgfR53ORnVv8j2snHyVmjHWEhdKk96shIiKiasiYvPbYwfhJxmD8+E5fycN7XyYBAL4Mc0arxkrk5utw/GLhzHD0hVzk5d//I2pvI0GXdhbo2tYCaSotPtuYhgK1CC93OT560wmuDuW+Zw0RERHVQlUWjHNycgy6UhSxty8+81cbMRhXjIUbU7HraDbqOcvg5S7HnxfzkK++/8fS2U6KLu0s0K2dBVp4mRn0Po65kY+ZK5KRptKhjpUEc99wgk8jhSk+BhEREVVDlRqMc3JyMG3aNGzatAlpaSV3FDDmznc1GYNxxcjM0WH03Lu4p9Lpt7k5ytCtnTm6tLNA8/pmEITS1xAn39Ng+opk/H1LDbkMCBtujz6drKqidCIiIqrmjMlrRv/cecKECfj222/x3HPPwdvbG2ZmZuUulAgArC0kmD7GERt3Z6BlQwW6tbNAo3ryh4bhBznZyfBlmAs+WZ+KI2dy8emGNNxM0OC15215Zz0iIiIqM6NnjJ2cnDB58mRMmjSpsmqqMThjXL3odCLW/pqBTb+pAACdW5tj2mgHmPOiPCIioidWpd4SGgDatWtXrsKIKpNEIuDV5+tg2mgHyGXAH+dy8fbniUhMY79jIiIiejSjg/HAgQOxd+/eyqiFqEL07miJLya6wM5Ggut31Bj/aQIuXs83dVlERERUzRm9lCIrKwuDBg1Cy5Yt0bdv3xI7ULRv377CCqzOuJSiektM02DGimT8c7vworxJLzvgaX9LU5dFREREVahSu1IkJydj6NChiIyMLHZxlCiKEASBXSmo2sjN0+Hjdan441wuAODtl+zwQndrE1dFREREVaVSu1K8+uqrOHHiBEJDQ9mVgqo9c6UEc153xNc/puOHA5n46vt7UCoEBAewnRsREREZMjoYR0ZGYtGiRRg3blxl1ENU4SQSASED60AUgf8dzMTnG9Ngbiaguy+XVRAREdF9Rl98Z21tjQYNGlRCKUSVRxAEvDmoDp59ygo6EfhobSqiz+eauiwiIiKqRowOxiNHjsSWLVsqoxaiSiUIAt4daodeHSyg1QGzVyXjr8vFb2lORERETyajl1K0adMG06dPxwsvvIB+/fqV2JVi4MCBFVIcUUWTSgRMHumAvAIRf5zNxYwVyVjwtjN8GilMXRoRERGZmNFdKSSSh08ysysF1QQFahEzViTjZEweLJUCPg91QVNPXkhKRERU21RqV4rIyMhyF0ZUXZjJBcx9wxGTlybj/N/5+OCrJHwx0Rle7gzHRERETyqjgnFeXh727NmDQYMGwdfXt7JqIqoSSjMJPn7TCZOWJOHKzQK8vyQJX4a5oK6z3NSlERERkQkYdfGdUqnEF198gezs7Mqqh6hKWZpL8OkEJzR0lyNNpcN7S5KQmKYxdVlERERkAkZ3pfD29kZsbGxl1EJkEjaWUix4xxn1nGVIStPi/SVJSMt4MtbJExER0X1GB+OZM2di/vz5+OeffyqjHiKTsLeR4vN3neHqIMXtJA3e/yoJGVkMx0RERE8So7tSPP/88zh16hSSk5PRunVruLm5QRCE+28oCPjpp58qvNDqiF0pap87yWqELkpCaoYWTT3NsOBtJ9hYSk1dFhEREZWTMXnN6GDcoEEDgyBc7A0FAdevXzfmLWssBuPa6Wa8GqFfJCIjS4f6rjJ8MsEZLvZGN3AhIiKiaqBSgzHdx2Bce8XeLcDkpclISdfCsY4Un7zlhIZ12cqNiIiopjEmrxm9xpjoSeDlboalk1xQ302OlHQt3l2UiLNXeftoIiKi2qxcwVitVmP16tUYPnw4+vTpg5dffhlr166FWq2u6PqITMbZXoYl77mgVWMFsnNFfLA0CYf+yjF1WURERFRJjF5KkZGRgV69euGvv/6CpaUlXF1dkZCQgOzsbPj6+uLAgQNPzLICLqV4MhSoRXy0NgVHzuRCEIC3BtthYA9rU5dVjCiK+Oe2Gvv+zEbUXzkQBOCpthbo1s4CLbzMIJGUfm0AERFRbVWpa4wnTJiA9evXY82aNXjxxRf12//3v/9h7NixGDVqFL766qvyVV7DMBg/ObQ6EUu/v4efDmcBAIY+bY3X+tepFmEzMU2DAyeysf/PHNyIL/mnNg62UjzV1hzd2lmgVWMFpNWgbiIioqpQqWuMd+zYgblz5xqEYgAYPHgwZs+ejR9//NHYt0RERAS8vLygVCrh6+uLI0eOPHR8VFQUfH19oVQq0bBhQ6xYscJg/8WLFzFo0CB9B43FixdXyHHpySWVCHhniB1efd4WALBlXyY+2ZAKtcY0165m5ujw6+9ZCF2UiGEz7uKbnzJwI14NuQzo2s4cc193xLwQRzzd0QKW5gJSM7T4KSoLYYuT8NLUO/jiuzScupwHjZbX3hIRERUxugdVUf/ikrRp0wYpKSlGvd/WrVsRGhqKiIgIdO7cGV9//TWCg4Nx6dIleHp6FhsfGxuLvn37Yty4cdi4cSP++OMPjB8/Hk5OThg0aBAAICcnBw0bNsSLL76IiRMnVshxiQRBwMvP2MLBVoqFm9Kw/88cpGfqMHucIyyUlX8da4FaxPGLudj/ZzaOXciFWlNUF9CmiQJPd7REl3YWsDK/X0vn1hYoUIv460oejpzOwR/ncnEvU4dffs/CL79nwcZSgs6tzdG1vQXaN1NCLuNMMhERPbmMXkrRsGFDjBo1CrNmzSq2b+7cuVi3bp1RfYz9/f3Rvn17LF++XL/N29sbAwYMQHh4eLHxkydPxs8//4yYmBj9tpCQEJw9exbR0dHFxjdo0AChoaEIDQ19rOOWhEspnlzHL+ZizqoU5BWIaOIhR/hbzrC3qZwbgcQlqvFjZCYOnMhGVu79v64N68rRu4MlevpZwLmMfZY1WhFnrubhyOlcHDmTg/QsnX6fjaUE3dpZoHdHC7RsqKgWy0SIiIgelzF5zegZ4yFDhuDjjz+GtbU1Ro0aBQcHB6SmpmLjxo34+OOPERYWVub3KigowKlTpzBlyhSD7UFBQTh69GiJr4mOjkZQUJDBtj59+mD16tVQq9WQy+WVclwAyM/PR35+vv65SqV65LGodvJvaY5Foc6YFpGMa7fUeHthIj6d4IR6zo/+81cWoijiryv5+N8BFY5fvN8mzrGOFL07WKBXB0s0qmd8X2WZVICftzn8vM3xzlA7nP87H1Gnc/D7mVykZmj1M8ku9lL07mCJXh0t0cCtYj4TERFRdWd0MJ49ezZOnz6NSZMm4f3334dMJoNGo4EoiujTpw9mz55d5vdKSUmBVquFi4uLwXYXFxckJCSU+JqEhIQSx2s0GqSkpMDNza1SjgsA4eHhmDNnziPfn54MzRso8NUkF3ywNBnxKRqEfJIAfx9zBPiYo0MLJWytjJ9BLlCL2P9nNrZFZiL2buGFdIIABLYyx4Du1mjbtOIunJNKBLRtqkTbpkpMeFHE2av52H8iG4dP5yAxTYtNe1TYtEeFxh73Z6Yd6/AOgEREVHsZ/b+cQqHAb7/9hj179iAyMhKpqalwcHBAr1698PTTT5eriP/eYloUxUfedvq/40vaXtHHnTp1qsGMuEqlgoeHh1HHpNqlrrMcX01ywfTlybhyswCRJ3MQeTIHEgFo0VCBTj5KBLQyRwM3+UP/bKVlaPHT4Uz8ciRLv7xBqRAQHGCJgd2tUbeCZqJLI5UIaN9cifbNlXh3iB2iL+Rh/5/Z+PNiLv6+pcbft9Lx9Y/paNdUgV4dLdG1rQUszXl/ICIiql3KPf3Tp08f9OnT57EO7ujoCKlUWmyWNikpqdhsbpGivsn/HS+TyeDg4FBpxwUKvylQKBRlOgY9OextpFj6vgtiYgtw7EIujl3IxfU7alz4Jx8X/snHNz9lwNleigAfc3RqZY52TZUwkxeG5L9vFWBbZCYOnszWX0znbC/FwO7W6BtoBSuLqg+fCjMJure3QPf2FsjI0iLqrxwcOJGD8//k468rhY8vt9yDb3Ml/LwLH/WcZUZ/Y1pbqbK1OHYhD93bW+jPsynFp2hw/U4BAlqZc904EdEjmPTnomZmZvD19cW+ffvwwgsv6Lfv27cP/fv3L/E1AQEB+OWXXwy27d27F35+fmVaX1ze4xI9jFQiwKeRAj6NFHitfx0kpmkKQ/L5XJy+mo+kNC1+OpyFnw5nQWkmoF0zJXLzdDhz7f6a9RZeZhjcywZd2phDKq0eAcbWSornu1rj+a7WSEgt6pecjZsJGkSfz0X0+VwAhWHez1sJv+ZKtGtWvmUktYFOJ+LDr1Nw7u98HD2Xg1mvOZr0G4a4RDXeWZgIVbYOga3NMXWUA2f6iYgeokxdKUprz1biGwoCzp49W+bxW7duxYgRI7BixQoEBARg5cqVWLVqFS5evIj69etj6tSpuHPnDjZs2ACgsF2bj48P3njjDYwbNw7R0dEICQnB5s2b9e3aCgoKcOnSJQBA37598fLLL+Pll1+GlZUVGjduXKbjlgW7UlBZ5BXocPpKPo6dL5xNTk7X6vdJJEC39hYY3MMa3l4146cRoiji+h01/ryUh5MxubjwT75+thsoXBPd1MMMft5K+Hor0bKh4olpA/dTVCa+3HpP//y1/rYY3sfWJLWkqbR4+7MExKfe//NW302O+SGOqOvECyqJ6MlR4Xe+6969+yNnPbKysnDq1CkIggCtVvvQsf8VERGBBQsWID4+Hj4+Pvjiiy/QtWtXAMDo0aNx48YNHDp0SD8+KioKEydOxMWLF+Hu7o7JkycjJCREv//GjRvw8vIqdpxu3boZvM/DjlsWDMZkrKJQeexCLnQi0Mffssyt1qqrvAIdzl3Lx6nLeTgZk6e/aLCIUiGgTWMFurW3QA9fCyjMaueMZUKqBmPnxyMvX0SHFkqcuJQHQQA+ecsJHVqYV2ktuXk6TFychKtxBXB3kmHCYDt8/l0aUjO0sLaQ4MPXHOHbXFmlNRERmUql3hL6vzQaDVauXIm5c+ciOTkZw4cPx7fffvs4b1ljMBgTFZeSrsFf/4bkU5fzcC/TsFdy30BLPNfFGm6ONfsbggeJoogPvkrGqct5aNVYgS9CnbFocxp2/ZENawsJlk9xhXsVfV6tVsSMFck4fjEPtlYSLJ3kgrrOcqSka/DhyhRcvlEAiQCEDKqDQT2suTaciGq9KgvGP/zwA6ZPn45//vkHvXv3xqeffoq2bduW9+1qHAZjoofT6UTE3lUj+nwudv6RhcS0wp8mCQLg31KJAd2s4eetrPEXhe36IwsLN6XBTC7gm+muqOcsR4FaxMQvEhFzowAN6xZ2LzFXVO5suSiKWPRdGnb+kQ2FXMCiUGeDJToFahFfbE7DnmPZAIA+nSwxcZh9tbhIkIioslR6MD506BAmT56MEydOoH379vj000/Rq1evchdcUzEYE5WdVifi2IVc/BSVhZMx929aUtdJhue7WuGZACtYl6MLh1YnQpWtg62lxCQBOzldg7Fz45GdJyJkYB281Pv+vwXJ9wr7W9/L1KGnnwWmj3Go1Bnab3dnYO0vGZAIwJw3HNG5tUWxMaIoYltkJlZsS4dOBLwbmGHO647sUU1EtValBePz589j8uTJ2LNnD7y8vDB//nwMHTr0sQuuqRiMicrnVqIaPx/Jwm/RWcj+9zbXCrmAXh0tMKCrNRp73L+rnyiKuKfSIT5Vg4R/H/GpGiSmahGfqkFSmgYaLeBgK0VgK3MEtjFsiVeZRFHE9OXJOHYhD80bmOGrSS7FbsBy9loeJn2ZBK0OeHNQHbzYq3L+rfgtOgsLvk0DALw71A79u1o/dPzJmFzMW52KzBwdHGylmPu6Y425AJSIyBgVHoxv3bqFGTNm4LvvvoO9vT1mzJiBkJCQMrdHq60YjIkeT26+DgdO5GBHVCau37l/0V4LLzNYWUiQkKJBQpoWBWrjfrBlrhDQoYUSga0t0MlHCRvLymkft//PbHy8LhVyGbBiiiu83Eu+Tff2yEws/eEeJBJgwdvOaN+sYi98O3EpF9MikqHVAcOCbDBuQJ0yve5Okhozvk7BzXg15DLgveH2COpkVaG1ERGZWoUHY3NzcxQUFOCZZ57BBx98AGvrh89EtG/f3riKaygGY6KKIYoiLvyTjx2Hs3D4rxxodYb7JQLgaCeFq70Mbo4yuDrI4Oog/fdXGWytJDh3LR9Hz+Xi6PlcpPynJV6rRgoEtjZHYGvzCmtVlpahxdj58VBl6zD2OVu8Elx6WzZRFPHphjTsPZ4NWysJlk92hatDxSxd+PtWAd5dlIjcfBG9O1hgyigHo5aU5OTpEL4uFX+cK+xJ/WIva7w+oE6l9dLWaEVIJcbfqZSIqLwqPBhLJPfX/T3sH7OiWyob266tpmIwJqp4aRlaHD6TA7lMgJuDDC4OUjjbycrcC1kURVyNK8Af53Jx9FyuwUw0ADRwk6Nza3P072b1WOtqZ69KxuHTuWhcT46Iya6QPSJI5hfo8M7nibh2S40mHnIsec/lsVvXJaRq8PbCRKRmaNG2qQKfTnAuV89onU7E+p0Z+Ha3CkDhjH3nNhZo5mmGJp5m5Vr7XSQjS4uL1wvvAnn+n3xcjSuAnbUU749wMHnLuOt3CvDZxjQ0cJNjwot2vPkJUS1V4cF4/fr1RhUwatQoo8bXVAzGRNVffIoGR8/l4Oj5XJy9lg/dv7PR1hYSvDvUDj39LI1+z6i/cjDnmxRIJcDyya4Ga6IfJiFVgzc/TUBGlg5B/paYPNK+3DOnmTk6vLMwATcTNPByl+PLMJfHvoV41F85+HRDKvIKDP9bcHeSoZmnGZp6mqFZfTM08TArMUSKoojENC3O/X0/CN+MVxcbBxR2JhkeZIPRz9qa5E6PJ2NyMXtVCnLyCj9rXScZZo9zRKN6ZTuXRFRzVGkf4ycZgzFRzZKZo8PxC7n438FMXI0rAAB0b2+Bd4falfk21hlZWoydF497mTq8EmyDsc/VMaqGv67k4YMlSdCJwNsv2eGF7g9fmlaSArWID75Kwrm/8+FYR4qlk1wq7EYxd5LUOHI2F1dvFuBKXAHiUzQljvNw+Tcs1zeDRBBw8XphEH5wGUsRTxcZfBor0KqRAs3rK7AtMhO//p4FAPBppMD0MQ5wqcIb3ew+moVF36VBqwNaNjRD8j0tku5pYSYX8M5LdggOtORSD6JahMG4ijAYE9VMGq2ITb+p8O3uDOh0gL2NBJNecUAnn0ffoe7jtSnYfyIHDdzkWDHFtVzdL77fr8KK7emQSoDPQ53RunHZlxTodCLmr0nFob9yYKkU8OV7LmhYt/JmOVXZWlyNK9AH5StxBUhKK325nEwKNPU0g0+jwiDcsqECdayLf9Nx6FQ2Pt+Uhuw8EdYWErz/ij2ealu8vVxFEkURa37JwKbfCpeM9O5ggUmvOCA3X4dP1qfi+MXCNoJB/pZ4d6hdpfedJqKqwWBcRRiMiWq2Kzfz8cn6VNxMKJwV7dvZEuMH2cFCWXIgOnouBzNWpEAiAF+97wLvBuVrbyaKIuavTUXkyRzYWUuwYoornOxkBvszsnSF7enStEhM1SAhTYPEVA3uJGtwK1EDmRT4ZELFd7goi/TMwrB85d/ArNGKaNmwMAg3a2AGZRnXTt9N0WDe6hRcuVk4e/9Cdyu88YJdpbTaK1CL+GxjKg6cyAEAvBJsgzHP2upnhnU6EVv2qbDm5wzoRKC+mxyzX3NEfbcnu/sSUW3AYFxFGIyJar78Ah1W/5yBbZGZEEXA1UGKySMd0KaJYeDMytFhzLx4pGZoMaS3Nd4YaPdYx83N1+HthYm4fkeNxvXk8PZSIPHf8JuYpi22zvdBUgkweaQDenc0fn10daPWiFj9czq+358JAGhcT46ZrzrCw6XiAqkqW4uZX6fg/N/5kEqAsOH2CA4suS3d2at5mL82FakZWigVAt4bbo9eHWr+15noScZgXEUYjIlqj7NX8/DJhlQkpmkhCMDgntZ49fk6+tnLz75Nxe7obNRzlmHVNNfH7igBAHeS1Rj/aSIyc3Ql7newlcLVQQoXe5m+NZ2LvRQN3OVwqmV3qjt2IRefbkhFRpYOSoWA0KH2CPJ//EB6N0WDqcuScCtRA0ulgFnjHOHn/fAlM2kqLT5am4LTV/IBAM89ZYW3XqycmWwiqnwMxlWEwZiodsnO1WH5tnvYdTQbQOGP06eOckBGlhaTlyZDEIDFE53Ryog1wY9y8Xo+dh/Ngr3tAwHYXgonO9kTF8SS0zUIX5uKM9cKA2mfTpZ45yU7mJeytOVRLsXmY8byZKRn6eBsJ8XH453KvB5bqxPx7a7CFnaiWDiTPWucY4X1wSaiqsNgXEUYjIlqp+jzuVi4KRX3VDpIJYCluQSqbB1e6G6Ft1+yN3V5tZpWV3hh5IadhWt9PVxkmPCiHRrVM4OdtaTM3SIOn87Bx+tSUaAW0dhDjo/fdCpX3+oTl3Lx8brCmWxLpYD3Rziga7vKvUiQag+1RkRqhhZpKi1S0rXIf8gSqZKYKwTUsZbC1koCWysJrMwlRt3ApyJl5+qgNBOqvL1idq4Od1M0iE/R6H8tegx52gbPPvXou3UyGFcRBmOi2isjS4svNqfh8OnCO8K5OkixerpbuWcvyThnr+bho3WpBu3fLJQC6jrJUM9FjnpOMtR1lsPDWYa6zjL9bb9FUcT/DmZixfZ0iCLg31KJD191fKzzlnxPg3lrUnHhn8KZ7GefssKrz9uWucXfk0CjFXH0XC5i76rRsYUSzRuY1fqWd6psLe4ka5CarkVqhhYpGVqkZdz/fWqGFhlZJS+TKi+JBIUh2VKKOlYS2FhJUMdKijrWEthYSmFnLYG9jRR2/z6szAWjzkNuvg53kzW4laTB7UQ1bidpcDup8FdVtg4SCeBUp/CnWy72Urg4yO7/3r7w92X5SZcoisjLF5GTLyInT4fcfBFZOYUXHBcF4KIQrMou/Wv4Yi9rvDno0dd7MBhXEQZjotpNFEUcPJmDfX9mY3Q/WzQvZxcKKp+MLC1WbE/H2Wt5SEzT4mH/W9lYSlDPWQZzhQSnLhe2XXuuixXeecmuQma4NFoRq39Kx9Z/LxK0sZRgzHO2ePYpK0hNNINXHcSnaLDzjyzsjs7CPdX9AOPpIkOfACs83dHise4wWV3odCJuJWoK7+J4PR8Xr+fjVmLJPb7/SyYtvF7AwVZqdAvAnDwdMrJ1yMjUIjvP+LgmlwF1rKT/hmUJ7Kz/Dc3WEtSxliJNpb0ffhM1SC6hD7mx7GwkcLGXwamOFFpd4WfIzRORk69DTp6I3PzCIGxM+qxjJYG7kwxujvcf7o4yeLrKYVdCO8j/YjCuIgzGRERVo0At4m6KBneS7s9i3UnS4HaypsSbirzxQh281Nu6wmctz17Nw1ff38P1u4V39GtUT463X7Izqhd1RUlO1+DI6VzUsZagUV0z1HOWVcmPuYtmh3f+kYWTMXn6gGNnI0GLBgqcjMlDvrpwo0QA/Foo8UwnSwS2tqiydfMFahEnYnJxN1mjn1G1sy78tY619JG3cM/N1+HKzQJc+KcwBF+KLSjxIlmnOlI41pHqg6+DrRQOdaRwfOC5jWXZlwA96jOpsgtnoTOydEjP0iI9UwdVduGvRc/TVFrcy9QiO7d88c7aQgIPFxnqOcv1v9ZzLgyj2bk6JKZpCzvopGke+H3hr3n5xh1TEAALhQBzpQQWCgEuDoWB98Hw6+YoK7WFZlkxGFcRBmMiItMr+vHv7aTCH722aGhWqUFVqxXx85EsrP0lHVn/ho9eHSzw+gt1qqRbSIFaxP8OqLBxj8ogiJjJBTRwk6Nh3cJHo7pmaFhXXmFLPhJSC2eHf4vORmrG/W9GfJsr8VwXKwS2NodMKiA7V4dDf+Vgz7Fs/fIToDBw9fSzQJ9OlmhWv+KXWmh1Is5dy8eBk9k4/FeO/tyUxNpCog/JdtaFyxHsbKRQZWtx8XoB/r5doL99fBGFXEDzBmZo2VABn4YKeHuZVevlNAVqEfcytbin0v4blnW4929ovqfSIT1TCxsrCTyc5aj3QAAu72cSRRGq7PvBOSVdC7lMgLlCgMW/wddcKYGFUoCFQgJzpQClmXFLPcqLwbiKMBgTET250jO1WPNLBnb+kQVRBJQKAa88Y4PBPW0qZWZUFEVEn89FxLZ03E0u/DF+Ew855DIB1++qS52tc6wj1QflBm5yWFtKYK4QYK6QQKkoDC7mZoXb/jvjrNUWHvPXP7Jw4tIDs8PWEjwTYIV+T1nB3bH0bwZuJ6mx51g29h7LNvgxfQM3Ofp0skSvDo+31EIURVy7pcaBE9mIPJVj8NMDB1spWjY0Q1aODumZOtzL0iIjUwddGVOPUx0pfP69e2PLhmZoVM/skTPNVD0xGFcRBmMiIroaV4AlW9NwKbbwDn71nGUYP9iuTLcYL6u4BDWW/e8eTlwqXD/tYCvF6wPqoHdHCwiCAJ1ORHyqBv/cVuP6nQL8c0eN63fUiE8p2zrYInIZHgjMEqiytQZrh32bK9HvKSt0bm0OuazsIVGrE3H6Sh72HMvGkTO5KFDfjx62VhJ4uBReSOnhWvjjew8XOdwdZaUG0TtJahw8mYP9J7IN1vpamQvo2t4CvTtYolVjRbH13zqdiMwcHe5lFs6Ypmdq9b+/l6mDmVxAS6/CWWFn+5q/NpoKMRhXEQZjIiICCgPX/hM5WPnjPaT9GyQ7+Sjx1mA71HUuf+/j7FwdNuzKwPbITGh1hRdyvdjLBi8/Y1OmdZfZuTrE3lXjnzsFuH5HjVsJauTkF14AlZd//0Io7UOaJ9SxkuCZAEv062z1WJ+lSFauDodO5eC36Cz9NxMlkUoAdyfZ/dDsIkduvg4HTubg8o37rzOTCwhoZY7eHSzQoYX5E9f/mx6NwbiKMBgTEdGDsnN1+HZ3BrYdLAyyclnhLGsTD7PCh6cZnO2kj1xXqdOJ2HM8G9/8lK6fse3ko8T4wXaoVwHh9L/Umvsh+cHALAhAy4YKo2aHjZGbp8PtZA3iEtS4lajGrSQNbiUUXmD5sNuiSwSgfXMlenWwwFNtLGBpzjaKVDoG4yrCYExERCWJS1Bj6Q/3cDImr9g+G0vJv0FZrg/L7o4y/Y0bYmLz8dUP9/SzopWxNKO60+lEpKRrcSvpgdCcqIFWK+Kpthbo7msBe5vqe+EbVS8MxlWEwZiIiEojiiKu3CxAzI0CXLtV+LhxV13isgULpYBG9cxgbSHB0XOFN5UxVwgY0dcWg3pYV9qMLdGTwJi8xpXlRERElUAQBDRvoDC4MUyBWkTs3QJcu6XWh+V/bhcgJ0/E+b/vtzbr08kSr/WvAwdbzooSVSUGYyIioipiJhfQrL4CzerfD8sarYi4BDX+vlWAuyka+Lc0h7cX77JIZAoMxkRERCYkkwpoWNcMDeuamboUoiceL+MkIiIiIgKDMRERERERAAZjIiIiIiIADMZERERERAAYjImIiIiIADAYExEREREBYDAmIiIiIgJQTYJxREQEvLy8oFQq4evriyNHjjx0fFRUFHx9faFUKtGwYUOsWLGi2Jht27ahRYsWUCgUaNGiBX788UeD/bNnz4YgCAYPV1fXCv1cRERERFRzmDwYb926FaGhoZg+fTpOnz6NLl26IDg4GHFxcSWOj42NRd++fdGlSxecPn0a06ZNwzvvvINt27bpx0RHR2PIkCEYMWIEzp49ixEjRuCll17C8ePHDd6rZcuWiI+P1z/Onz9fqZ+ViIiIiKovQRRF0ZQF+Pv7o3379li+fLl+m7e3NwYMGIDw8PBi4ydPnoyff/4ZMTEx+m0hISE4e/YsoqOjAQBDhgyBSqXC7t279WOeeeYZ2NnZYfPmzQAKZ4x37NiBM2fOlLt2lUoFW1tbZGRkwMbGptzvQ0RERESVw5i8ZtIZ44KCApw6dQpBQUEG24OCgnD06NESXxMdHV1sfJ8+fXDy5Emo1eqHjvnve167dg3u7u7w8vLC0KFDcf369YfWm5+fD5VKZfAgIiIiotrBpME4JSUFWq0WLi4uBttdXFyQkJBQ4msSEhJKHK/RaJCSkvLQMQ++p7+/PzZs2IA9e/Zg1apVSEhIQGBgIFJTU0utNzw8HLa2tvqHh4eHUZ+XiIiIiKovk68xBgBBEAyei6JYbNujxv93+6PeMzg4GIMGDUKrVq3Qu3dv7Ny5EwCwfv36Uo87depUZGRk6B+3bt16xCcjIiIioppCZsqDOzo6QiqVFpsdTkpKKjbjW8TV1bXE8TKZDA4ODg8dU9p7AoClpSVatWqFa9eulTpGoVBAoVA89DMRERERUc1k0hljMzMz+Pr6Yt++fQbb9+3bh8DAwBJfExAQUGz83r174efnB7lc/tAxpb0nULh+OCYmBm5ubuX5KERERERUw5l8KUVYWBi++eYbrFmzBjExMZg4cSLi4uIQEhICoHD5wsiRI/XjQ0JCcPPmTYSFhSEmJgZr1qzB6tWrMWnSJP2Yd999F3v37sWnn36Ky5cv49NPP8X+/fsRGhqqHzNp0iRERUUhNjYWx48fx+DBg6FSqTBq1Kgq++xEREREVH2YdCkFUNhaLTU1FXPnzkV8fDx8fHywa9cu1K9fHwAQHx9v0NPYy8sLu3btwsSJE7Fs2TK4u7tjyZIlGDRokH5MYGAgtmzZghkzZmDmzJlo1KgRtm7dCn9/f/2Y27dvY9iwYUhJSYGTkxM6deqEY8eO6Y9LRERERE8Wk/cxrsnYx5iIiIioeqsxfYyJiIiIiKoLBmMiIiIiIjAYExEREREBYDAmIiIiIgLAYExEREREBIDBmIiIiIgIAIMxEREREREABmMiIiIiIgAMxkREREREABiMiYiIiIgAMBgTEREREQFgMCYiIiIiAsBgTEREREQEgMGYiIiIiAgAgzEREREREQAGYyIiIiIiAAzGREREREQAGIyJiIiIiAAwGBMRERERAWAwJiIiIiICwGBMRERERASAwZiIiIiICACDMRERERERAAZjIiIiIiIADMZERERERAAYjImIiIiIADAYExEREREBYDAmIiIiIgLAYExEREREBIDBmIiIiIgIAIMxEREREREABmMiIiIiIgAMxkREREREABiMiYiIiIgAVJNgHBERAS8vLyiVSvj6+uLIkSMPHR8VFQVfX18olUo0bNgQK1asKDZm27ZtaNGiBRQKBVq0aIEff/zxsY9LRERERLWXyYPx1q1bERoaiunTp+P06dPo0qULgoODERcXV+L42NhY9O3bF126dMHp06cxbdo0vPPOO9i2bZt+THR0NIYMGYIRI0bg7NmzGDFiBF566SUcP3683MclIiIiotpNEEVRNGUB/v7+aN++PZYvX67f5u3tjQEDBiA8PLzY+MmTJ+Pnn39GTEyMfltISAjOnj2L6OhoAMCQIUOgUqmwe/du/ZhnnnkGdnZ22Lx5c7mOWxKVSgVbW1tkZGTAxsbGuA9ORERERJXOmLwmq6KaSlRQUIBTp05hypQpBtuDgoJw9OjREl8THR2NoKAgg219+vTB6tWroVarIZfLER0djYkTJxYbs3jx4nIfFwDy8/ORn5+vf56RkQGg8AtORERERNVPUU4ry1ywSYNxSkoKtFotXFxcDLa7uLggISGhxNckJCSUOF6j0SAlJQVubm6ljil6z/IcFwDCw8MxZ86cYts9PDxK/5BEREREZHKZmZmwtbV96BiTBuMigiAYPBdFsdi2R43/7/ayvKexx506dSrCwsL0z3U6HdLS0uDg4PDQ1xVRqVTw8PDArVu3uPSiluG5rb14bms3nt/ai+e29jL23IqiiMzMTLi7uz9yrEmDsaOjI6RSabFZ2qSkpGKzuUVcXV1LHC+TyeDg4PDQMUXvWZ7jAoBCoYBCoTDYVqdOndI/YClsbGz4l7SW4rmtvXhuazee39qL57b2MubcPmqmuIhJu1KYmZnB19cX+/btM9i+b98+BAYGlviagICAYuP37t0LPz8/yOXyh44pes/yHJeIiIiIajeTL6UICwvDiBEj4Ofnh4CAAKxcuRJxcXEICQkBULh84c6dO9iwYQOAwg4US5cuRVhYGMaNG4fo6GisXr1a320CAN5991107doVn376Kfr374+ffvoJ+/fvx++//17m4xIRERHRk8XkwXjIkCFITU3F3LlzER8fDx8fH+zatQv169cHAMTHxxv0Fvby8sKuXbswceJELFu2DO7u7liyZAkGDRqkHxMYGIgtW7ZgxowZmDlzJho1aoStW7fC39+/zMetDAqFArNmzSq2HINqPp7b2ovntnbj+a29eG5rr8o8tybvY0xEREREVB2Y/M53RERERETVAYMxEREREREYjImIiIiIADAYExEREREBYDCuMhEREfDy8oJSqYSvry+OHDli6pKoHA4fPoznnnsO7u7uEAQBO3bsMNgviiJmz54Nd3d3mJubo3v37rh48aJpiiWjhIeHo0OHDrC2toazszMGDBiAK1euGIzh+a2Zli9fjtatW+tvBhAQEIDdu3fr9/O81h7h4eEQBAGhoaH6bTy/Ndfs2bMhCILBw9XVVb+/Ms4tg3EV2Lp1K0JDQzF9+nScPn0aXbp0QXBwsEEbOqoZsrOz0aZNGyxdurTE/QsWLMCiRYuwdOlSnDhxAq6urnj66aeRmZlZxZWSsaKiovDWW2/h2LFj2LdvHzQaDYKCgpCdna0fw/NbM9WrVw+ffPIJTp48iZMnT6Jnz57o37+//j9Qntfa4cSJE1i5ciVat25tsJ3nt2Zr2bIl4uPj9Y/z58/r91XKuRWp0nXs2FEMCQkx2Na8eXNxypQpJqqIKgIA8ccff9Q/1+l0oqurq/jJJ5/ot+Xl5Ym2trbiihUrTFAhPY6kpCQRgBgVFSWKIs9vbWNnZyd+8803PK+1RGZmptikSRNx3759Yrdu3cR3331XFEX+va3pZs2aJbZp06bEfZV1bjljXMkKCgpw6tQpBAUFGWwPCgrC0aNHTVQVVYbY2FgkJCQYnGuFQoFu3brxXNdAGRkZAAB7e3sAPL+1hVarxZYtW5CdnY2AgACe11rirbfeQr9+/dC7d2+D7Ty/Nd+1a9fg7u4OLy8vDB06FNevXwdQeefW5He+q+1SUlKg1Wrh4uJisN3FxQUJCQkmqooqQ9H5LOlc37x50xQlUTmJooiwsDA89dRT8PHxAcDzW9OdP38eAQEByMvLg5WVFX788Ue0aNFC/x8oz2vNtWXLFvz11184ceJEsX38e1uz+fv7Y8OGDWjatCkSExMxf/58BAYG4uLFi5V2bhmMq4ggCAbPRVEsto1qB57rmm/ChAk4d+4cfv/992L7eH5rpmbNmuHMmTNIT0/Htm3bMGrUKERFRen387zWTLdu3cK7776LvXv3QqlUljqO57dmCg4O1v++VatWCAgIQKNGjbB+/Xp06tQJQMWfWy6lqGSOjo6QSqXFZoeTkpKKfZdDNVvRlbI81zXb22+/jZ9//hmRkZGoV6+efjvPb81mZmaGxo0bw8/PD+Hh4WjTpg2+/PJLntca7tSpU0hKSoKvry9kMhlkMhmioqKwZMkSyGQy/Tnk+a0dLC0t0apVK1y7dq3S/u4yGFcyMzMz+Pr6Yt++fQbb9+3bh8DAQBNVRZXBy8sLrq6uBue6oKAAUVFRPNc1gCiKmDBhArZv346DBw/Cy8vLYD/Pb+0iiiLy8/N5Xmu4Xr164fz58zhz5oz+4efnh5dffhlnzpxBw4YNeX5rkfz8fMTExMDNza3S/u5yKUUVCAsLw4gRI+Dn54eAgACsXLkScXFxCAkJMXVpZKSsrCz8/fff+uexsbE4c+YM7O3t4enpidDQUHz88cdo0qQJmjRpgo8//hgWFhYYPny4Caumsnjrrbfw3Xff4aeffoK1tbV+FsLW1hbm5ub63qg8vzXPtGnTEBwcDA8PD2RmZmLLli04dOgQfvvtN57XGs7a2lp/HUARS0tLODg46Lfz/NZckyZNwnPPPQdPT08kJSVh/vz5UKlUGDVqVOX93S13PwsyyrJly8T69euLZmZmYvv27fUtoKhmiYyMFAEUe4waNUoUxcL2MbNmzRJdXV1FhUIhdu3aVTx//rxpi6YyKem8AhDXrl2rH8PzWzONHTtW/++vk5OT2KtXL3Hv3r36/TyvtcuD7dpEkee3JhsyZIjo5uYmyuVy0d3dXRw4cKB48eJF/f7KOLeCKIriYwZ6IiIiIqIaj2uMiYiIiIjAYExEREREBIDBmIiIiIgIAIMxEREREREABmMiIiIiIgAMxkREREREABiMiYiIiIgAMBgTEREREQFgMCYiqvbWrVsHQRBKfRw6dMhktd24cQOCIGDhwoUmq4GIqKLITF0AERGVzdq1a9G8efNi21u0aGGCaoiIah8GYyKiGsLHxwd+fn6mLoOIqNbiUgoiolpCEARMmDABX3/9NZo2bQqFQoEWLVpgy5YtxcZeuHAB/fv3h52dHZRKJdq2bYv169cXG5eeno733nsPDRs2hEKhgLOzM/r27YvLly8XG7to0SJ4eXnBysoKAQEBOHbsmMH+69evY+jQoXB3d4dCoYCLiwt69eqFM2fOVNjXgIjocXDGmIiohtBqtdBoNAbbBEGAVCrVP//5558RGRmJuXPnwtLSEhERERg2bBhkMhkGDx4MALhy5QoCAwPh7OyMJUuWwMHBARs3bsTo0aORmJiIDz74AACQmZmJp556Cjdu3MDkyZPh7++PrKwsHD58GPHx8QbLOpYtW4bmzZtj8eLFAICZM2eib9++iI2Nha2tLQCgb9++0Gq1WLBgATw9PZGSkoKjR48iPT29Er9qRERlJ4iiKJq6CCIiKt26deswZsyYEvdJpVJ9WBYEAebm5oiNjYWLiwuAwjDt4+MDjUaDa9euAQCGDRuGH3/8EdeuXYOHh4f+vfr27YuoqCjcvXsXtra2mDdvHj788EPs27cPvXv3LvH4N27cgJeXF1q1aoXTp0/rQ/qJEyfQsWNHbN68GUOHDkVqaiocHR2xePFivPvuuxX2tSEiqkicMSYiqiE2bNgAb29vg22CIBg879Wrlz4UA4XBeciQIZgzZw5u376NevXq4eDBg+jVq5dBKAaA0aNHY/fu3YiOjsYzzzyD3bt3o2nTpqWG4gf169fPYOa6devWAICbN28CAOzt7dGoUSN89tln0Gq16NGjB9q0aQOJhCv6iKj64L9IREQ1hLe3N/z8/Awevr6+BmNcXV2Lva5oW2pqqv5XNze3YuPc3d0NxiUnJ6NevXplqs3BwcHguUKhAADk5uYCKAzwBw4cQJ8+fbBgwQK0b98eTk5OeOedd5CZmVmmYxARVTbOGBMR1SIJCQmlbisKrw4ODoiPjy827u7duwAAR0dHAICTkxNu375dYbXVr18fq1evBgBcvXoV33//PWbPno2CggKsWLGiwo5DRFRenDEmIqpFDhw4gMTERP1zrVaLrVu3olGjRvrZ3169euHgwYP6IFxkw4YNsLCwQKdOnQAAwcHBuHr1Kg4ePFjhdTZt2hQzZsxAq1at8Ndff1X4+xMRlQdnjImIaogLFy4U60oBAI0aNYKTkxOAwtnenj17YubMmfquFJcvXzZo2TZr1iz8+uuv6NGjBz788EPY29tj06ZN2LlzJxYsWKDvIhEaGoqtW7eif//+mDJlCjp27Ijc3FxERUXh2WefRY8ePcpc+7lz5zBhwgS8+OKLaNKkCczMzHDw4EGcO3cOU6ZMecyvDBFRxWAwJiKqIUrrTLFq1Sq89tprAIDnn38eLVu2xIwZMxAXF4dGjRph06ZNGDJkiH58s2bNcPToUUybNg1vvfUWcnNz4e3tjbVr12L06NH6cdbW1vj9998xe/ZsrFy5EnPmzIGdnR06dOiA119/3ajaXV1d0ahRI0RERODWrVsQBAENGzbE559/jrffftv4LwYRUSVguzYiolpCEAS89dZbWLp0qalLISKqkbjGmIiIiIgIDMZERERERAC4xpiIqNbgyjgiosfDGWMiIiIiIjAYExEREREBYDAmIiIiIgLAYExEREREBIDBmIiIiIgIAIMxEREREREABmMiIiIiIgAMxkREREREAID/A8oRn9Tq3RWsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:23<00:00,  1.57s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg Regret: 0.2265\n",
            "Avg Rel Regret: 0.94%\n",
            "Path Accuracy: 96.87%\n",
            "Optimality Ratio: 75.60%\n"
          ]
        }
      ],
      "source": [
        "# plot\n",
        "plotLearningCurve(loss_log3, regret_log3)\n",
        "# eval\n",
        "print(\"Test set:\")\n",
        "df3 = evaluate(nnet, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd15a583",
      "metadata": {
        "id": "dd15a583"
      },
      "source": [
        "### 6.4 DBB"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "509eb432",
      "metadata": {
        "id": "509eb432"
      },
      "source": [
        "DBB model: training with differentiable black-box optimizer and MSE of solutions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00de53b7",
      "metadata": {
        "id": "00de53b7"
      },
      "outputs": [],
      "source": [
        "# init net\n",
        "nnet = partialResNet(k=12)\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    nnet = nnet.cuda()\n",
        "# set optimizer\n",
        "optimizer = torch.optim.Adam(nnet.parameters(), lr=1e-5)\n",
        "# set stopper\n",
        "#stopper = earlyStopper(patience=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6f982a1f",
      "metadata": {
        "id": "6f982a1f",
        "outputId": "9bd35b56-0d7d-47c5-c636-acdbb4fa0b41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of cores: 1\n"
          ]
        }
      ],
      "source": [
        "# init dbb\n",
        "dbb = pyepo.func.blackboxOpt(optmodel, lambd=10, processes=1)\n",
        "# set loss\n",
        "class hammingLoss(torch.nn.Module):\n",
        "    def forward(self, wp, w):\n",
        "        loss = wp * (1.0 - w) + (1.0 - wp) * w\n",
        "        return loss.mean(dim=0).sum()\n",
        "hmloss = hammingLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "071cf53e",
      "metadata": {
        "id": "071cf53e",
        "outputId": "0d2e5d23-c89b-4949-e52e-ac3b8b1b8542"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch: 49, Loss: 10.2667: 100%|██████████████████████████████████████████████████████████████████████| 50/50 [6:52:58<00:00, 495.56s/it]\n"
          ]
        }
      ],
      "source": [
        "# train\n",
        "loss_log4, regret_log4 = [], [pyepo.metric.regret(nnet, optmodel, loader_test)]\n",
        "tbar = tqdm(range(epochs))\n",
        "for epoch in tbar:\n",
        "    nnet.train()\n",
        "    for x, c, w, z in loader_train:\n",
        "        # cuda\n",
        "        if torch.cuda.is_available():\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # forward pass\n",
        "        cp = nnet(x) # predicted cost\n",
        "        wp = dbb(cp) # black-box optimizer\n",
        "        loss = hmloss(wp, w) # loss\n",
        "        # backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # log\n",
        "        loss_log4.append(loss.item())\n",
        "        tbar.set_description(\"Epoch: {:2}, Loss: {:3.4f}\".format(epoch, loss.item()))\n",
        "    # scheduled learning rate\n",
        "    if (epoch == int(epochs*0.6)) or (epoch == int(epochs*0.8)):\n",
        "        for g in optimizer.param_groups:\n",
        "            g['lr'] /= 10\n",
        "    if epoch % log_step == 0:\n",
        "        # log regret\n",
        "        regret = pyepo.metric.regret(nnet, optmodel, loader_test) # regret on test\n",
        "        regret_log4.append(regret)\n",
        "        # early stop\n",
        "        #regret = pyepo.metric.regret(nnet, optmodel, loader_val) # regret on val\n",
        "        #if stopper.stop(regret):\n",
        "        #    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6dbba91",
      "metadata": {
        "id": "a6dbba91",
        "outputId": "d9398d54-5d2c-4439-a0f5-7dccd0aa78fd"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:24<00:00,  1.61s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg Regret: 2.4579\n",
            "Avg Rel Regret: 10.01%\n",
            "Path Accuracy: 93.20%\n",
            "Optimality Ratio: 26.50%\n"
          ]
        }
      ],
      "source": [
        "# plot\n",
        "plotLearningCurve(loss_log4,  regret_log4)\n",
        "# eval\n",
        "print(\"Test set:\")\n",
        "df4 = evaluate(nnet, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74fd3e7f",
      "metadata": {
        "id": "74fd3e7f"
      },
      "source": [
        "### 6.5 DPO"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5730bace",
      "metadata": {
        "id": "5730bace"
      },
      "source": [
        "DPO model: training with differentiable perturbed optimizer and MSE of solutions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7688ea5",
      "metadata": {
        "id": "e7688ea5"
      },
      "outputs": [],
      "source": [
        "# init net\n",
        "nnet = partialResNet(k=12)\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    nnet = nnet.cuda()\n",
        "# set optimizer\n",
        "optimizer = torch.optim.Adam(nnet.parameters(), lr=lr)\n",
        "# set stopper\n",
        "#stopper = earlyStopper(patience=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c344cf6",
      "metadata": {
        "id": "2c344cf6",
        "outputId": "291adb81-729d-44b1-c04b-8760b3b68393"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of cores: 1\n"
          ]
        }
      ],
      "source": [
        "# init dpo\n",
        "ptb = pyepo.func.perturbedOpt(optmodel, n_samples=1, sigma=1.0, processes=1)\n",
        "# set loss\n",
        "mseloss = nn.MSELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ef6cfe80",
      "metadata": {
        "id": "ef6cfe80",
        "outputId": "7cb86127-d353-4b9e-e31c-1194ffeda9b8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch: 49, Loss: 0.1216: 100%|███████████████████████████████████████████████████████████████████████| 50/50 [3:43:37<00:00, 268.35s/it]\n"
          ]
        }
      ],
      "source": [
        "# train\n",
        "loss_log5, regret_log5 = [], [pyepo.metric.regret(nnet, optmodel, loader_test)]\n",
        "tbar = tqdm(range(epochs))\n",
        "for epoch in tbar:\n",
        "    nnet.train()\n",
        "    for x, c, w, z in loader_train:\n",
        "        # cuda\n",
        "        if torch.cuda.is_available():\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # forward pass\n",
        "        cp = nnet(x) # predicted cost\n",
        "        we = ptb(cp) # perturbed optimizer\n",
        "        loss = mseloss(we, w) # loss\n",
        "        # backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # log\n",
        "        loss_log5.append(loss.item())\n",
        "        tbar.set_description(\"Epoch: {:2}, Loss: {:3.4f}\".format(epoch, loss.item()))\n",
        "    # scheduled learning rate\n",
        "    if (epoch == int(epochs*0.6)) or (epoch == int(epochs*0.8)):\n",
        "        for g in optimizer.param_groups:\n",
        "            g['lr'] /= 10\n",
        "    if epoch % log_step == 0:\n",
        "        # log regret\n",
        "        regret = pyepo.metric.regret(nnet, optmodel, loader_test) # regret on test\n",
        "        regret_log5.append(regret)\n",
        "        # early stop\n",
        "        #regret = pyepo.metric.regret(nnet, optmodel, loader_val) # regret on val\n",
        "        #if stopper.stop(regret):\n",
        "        #    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5db27ae",
      "metadata": {
        "id": "c5db27ae",
        "outputId": "7ef1052d-1cf2-49fe-db5f-8bd2bc1e5318"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:23<00:00,  1.57s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg Regret: 11.2376\n",
            "Avg Rel Regret: 47.51%\n",
            "Path Accuracy: 88.56%\n",
            "Optimality Ratio: 7.20%\n"
          ]
        }
      ],
      "source": [
        "# plot\n",
        "plotLearningCurve(loss_log5, regret_log5)\n",
        "# eval\n",
        "print(\"Test set:\")\n",
        "df5 = evaluate(nnet, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a9b4904",
      "metadata": {
        "id": "1a9b4904"
      },
      "source": [
        "### 6.6 PFYL"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6bf900e4",
      "metadata": {
        "id": "6bf900e4"
      },
      "source": [
        "PFYL model: training with Perturbed Fenchel-Young loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3378864b",
      "metadata": {
        "id": "3378864b"
      },
      "outputs": [],
      "source": [
        "# init net\n",
        "nnet = partialResNet(k=12)\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    nnet = nnet.cuda()\n",
        "# set optimizer\n",
        "optimizer = torch.optim.Adam(nnet.parameters(), lr=lr)\n",
        "# set stopper\n",
        "#stopper = earlyStopper(patience=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "486cdfaf",
      "metadata": {
        "id": "486cdfaf",
        "outputId": "234dd7e3-6b94-4b99-c71f-9738679a54dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num of cores: 1\n"
          ]
        }
      ],
      "source": [
        "# set loss\n",
        "fyloss = pyepo.func.perturbedFenchelYoung(optmodel, n_samples=1, sigma=1.0, processes=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87e2649d",
      "metadata": {
        "id": "87e2649d",
        "outputId": "bd338d2a-cece-4b65-9ce2-2dc81fd48d42"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Epoch: 49, Loss: 2.3500: 100%|███████████████████████████████████████████████████████████████████████| 50/50 [3:41:13<00:00, 265.47s/it]\n"
          ]
        }
      ],
      "source": [
        "# train\n",
        "loss_log6, regret_log6 = [], [pyepo.metric.regret(nnet, optmodel, loader_test)]\n",
        "tbar = tqdm(range(epochs))\n",
        "for epoch in tbar:\n",
        "    nnet.train()\n",
        "    for x, c, w, z in loader_train:\n",
        "        # cuda\n",
        "        if torch.cuda.is_available():\n",
        "            x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "        # forward pass\n",
        "        cp = nnet(x) # predicted cost\n",
        "        loss = fyloss(cp, w).mean() # loss\n",
        "        # backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # log\n",
        "        loss_log6.append(loss.item())\n",
        "        tbar.set_description(\"Epoch: {:2}, Loss: {:3.4f}\".format(epoch, loss.item()))\n",
        "    # scheduled learning rate\n",
        "    if (epoch == int(epochs*0.6)) or (epoch == int(epochs*0.8)):\n",
        "        for g in optimizer.param_groups:\n",
        "            g['lr'] /= 10\n",
        "    if epoch % log_step == 0:\n",
        "        # log regret\n",
        "        regret = pyepo.metric.regret(nnet, optmodel, loader_test) # regret on test\n",
        "        regret_log6.append(regret)\n",
        "        # early stop\n",
        "        #regret = pyepo.metric.regret(nnet, optmodel, loader_val) # regret on val\n",
        "        #if stopper.stop(regret):\n",
        "        #    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13864fbc",
      "metadata": {
        "id": "13864fbc",
        "outputId": "4d76cdf9-3e87-42b9-a0c0-9f0876481012"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test set:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:23<00:00,  1.58s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Avg Regret: 0.3865\n",
            "Avg Rel Regret: 1.42%\n",
            "Path Accuracy: 97.56%\n",
            "Optimality Ratio: 79.90%\n"
          ]
        }
      ],
      "source": [
        "# plot\n",
        "plotLearningCurve(loss_log6, regret_log6)\n",
        "# eval\n",
        "print(\"Test set:\")\n",
        "df6 = evaluate(nnet, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f7a41ea0",
      "metadata": {
        "id": "f7a41ea0"
      },
      "source": [
        "## 7 Comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "db133936",
      "metadata": {
        "id": "db133936"
      },
      "outputs": [],
      "source": [
        "# colors\n",
        "colors = [\"#dd604d\", \"#ee8866\", \"#77aadd\", \"#5aa461\", \"#9970ab\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8898a0ee",
      "metadata": {
        "id": "8898a0ee"
      },
      "source": [
        "### 7.1 Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "17510694",
      "metadata": {
        "id": "17510694",
        "outputId": "e13e0099-e905-485f-93e9-26006ee0973a"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# drow learning curve on test set\n",
        "fig = plt.figure(figsize=(16,6))\n",
        "plt.plot([i*log_step for i in range(len(regret_log1))], regret_log1, color=colors[0], lw=3, ls=\"-\", label=\"2S\")\n",
        "plt.plot([i*log_step for i in range(len(regret_log3))], regret_log3, color=colors[1], lw=3, ls=\"--\", label=\"SPO+\", )\n",
        "plt.plot([i*log_step for i in range(len(regret_log4))], regret_log4, color=colors[2], lw=3, ls=\"-.\", label=\"DBB\")\n",
        "plt.plot([i*log_step for i in range(len(regret_log5))], regret_log5, color=colors[3], lw=3, ls=\":\", label=\"DPO\")\n",
        "plt.plot([i*log_step for i in range(len(regret_log6))], regret_log6, color=colors[4], lw=3, ls=(0,(3,1,1,1,1,1)), label=\"PFYL\")\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.xlim(-1, epochs+1)\n",
        "plt.ylim(-0.05, 1.95)\n",
        "plt.xlabel(\"Epochs\", fontsize=20)\n",
        "plt.ylabel(\"Normalized Regret\", fontsize=20)\n",
        "plt.title(\"Learning Curve on Test Set\", fontsize=20)\n",
        "plt.legend(fontsize=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83531675",
      "metadata": {
        "id": "83531675"
      },
      "source": [
        "### 7.2 Regret"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3e59e42",
      "metadata": {
        "id": "b3e59e42",
        "outputId": "5e987ded-a1b8-41bc-e408-5532384e59c6"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# draw boxplot of regret per instance\n",
        "fig = plt.figure(figsize=(16,6))\n",
        "boxplot_data = [df1[\"Regret\"], df3[\"Regret\"], df4[\"Regret\"], df5[\"Regret\"], df6[\"Regret\"]]\n",
        "bp = plt.boxplot(boxplot_data, medianprops=dict(color=\"dimgrey\", linewidth=2), patch_artist=True, widths=0.75)\n",
        "for i, patch in enumerate(bp[\"boxes\"]):\n",
        "    patch.set_facecolor(colors[i])\n",
        "    patch.set_color(colors[i])\n",
        "    patch.set_linewidth(4)\n",
        "for i, patch in enumerate(bp[\"whiskers\"]):\n",
        "    patch.set_color(colors[i//2])\n",
        "    patch.set_linewidth(2)\n",
        "for i, patch in enumerate(bp[\"caps\"]):\n",
        "    patch.set_color(colors[i//2])\n",
        "    patch.set_linewidth(3)\n",
        "for i, patch in enumerate(bp[\"fliers\"]):\n",
        "    patch.set_marker(\"o\")\n",
        "    patch.set_markeredgecolor(colors[i])\n",
        "    patch.set_markersize(6)\n",
        "    patch.set_markeredgewidth(2)\n",
        "for i, patch in enumerate(bp[\"medians\"]):\n",
        "    patch.set_color(\"w\")\n",
        "    patch.set_linewidth(2)\n",
        "# grid\n",
        "plt.grid(color=\"grey\", alpha=0.5, linewidth=0.5, which=\"major\", axis=\"y\")\n",
        "# labels and ticks\n",
        "plt.xticks(ticks=range(1,6), fontsize=16, labels=[\"2S\", \"SPO+\", \"DBB\", \"DPO\", \"PFYL\"])\n",
        "plt.xlabel(\"Methods\", fontsize=20)\n",
        "plt.ylabel(\"Regret\", fontsize=20)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.xlim(0.5, 5.5)\n",
        "plt.ylim(-0.2, 40)\n",
        "plt.title(\"Regret for each Instance on Test Set\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "036eac0f",
      "metadata": {
        "id": "036eac0f"
      },
      "source": [
        "### 7.3 Relative Regret"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9a71f36",
      "metadata": {
        "id": "b9a71f36",
        "outputId": "e3b45a9a-dbf0-4490-84d6-ee83b54774e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABUgAAAJCCAYAAAASvi1KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADI+0lEQVR4nOzdd3gU1f7H8c9sdlNISAIJQXpAioKogAQFFSxXRVFQFBGQIteCclGxXQEVxHYtiHqxIYgFwYL8LCgKglcpSsdIk94RCIQQQpIt5/dHzJolbZPdJIS8X8+zD5mZc+Z8Z3aSDd+cYhljjAAAAAAAAACgCrJVdAAAAAAAAAAAUFFIkAIAAAAAAACoskiQAgAAAAAAAKiySJACAAAAAAAAqLJIkAIAAAAAAACoskiQAgAAAAAAAKiySJACAAAAAAAAqLJIkAIAAAAAAACoskiQAgAAAAAAAKiySJACAKqkbdu2ybIsWZalKVOmVHQ4Gj16tDceoCwsXLhQ119/vU477TTZ7Xbv85aamlrRoZ3SpkyZ4r3X27Ztq+hwAAAAUAASpACAcvPjjz96EwUnviIiIlS/fn117dpVr7/+utLT0ys6XJRC3sTzia+wsDCddtppuvTSS/Wf//xHBw8erOhwq4yvvvpKnTt31v/93//pzz//lNvtruiQUAYGDhxIMhYnrcI+G0ryOpWe640bN+rhhx9WUlKSatasKYfDofj4eLVs2VJXXHGFRo8erR9//FGZmZkVHSoAVAkkSAEAJ4XMzEzt3r1bs2fP1j333KOzzjpLq1evruiwAkLPMV/Z2dn6888/NX/+fP373//WmWeeqblz51Z0WCedsnhuHnjgAbndbtWtW1fvv/++li9fruTkZCUnJys6OjoobQDF4WciKqOyGOHx9NNPq2XLlnrhhRe0dOlSHT58WC6XSykpKVq3bp3mzJmjMWPG6JJLLtHo0aOD1u6Jcq+rLNsAgMrCXtEBAACqpiFDhujuu+/2bh88eFAbNmzQuHHj9Mcff2j79u3q2rWrNmzYoOrVq1dgpOVj9OjRp9x/ULp3766nnnrKu52WlqY//vhDEyZM0LJly3Tw4EFdf/31+v3339WoUaMKjPTUtmPHDm3cuFGSNGLECN16660VHBGAqig5ObnQY4MGDdKyZcuKLVevXr2gx1XeXnjhBY0aNUqSFBMTozvvvFOdO3dWnTp1lJ2drV27dunXX3/VV199pfXr11dwtABQdZAgBQBUiISEBJ111lk++7p06aJBgwapa9eumjdvnvbu3au3335bDzzwQAVFiUDExsbme487duyoAQMGaODAgXr//feVnp6uF198Ua+99loFRXnq2717t/fr5s2bV2AkAKqyEz8P8oqMjPSrXGWXkpKiJ554QpJUv359LVq0SA0aNPAp06FDB/Xs2VPPP/+8lixZopSUlIoIFQCqHIbYAwBOKqGhoT49KefMmVNxwaBMWJbl07OU97hsZWVleb92OBwVGAkAVG3ff/+9jh8/Lkn697//nS85eqKkpCR17dq1PEIDgCqPBCkA4KTTtm1b79c7d+4ssuyGDRs0bNgwtWrVSjExMYqIiFCTJk00aNAgrVixIqA4fv/9dz311FO68sorVb9+fYWFhSkqKkrNmjXTgAED9MsvvxRYL3cxqkGDBnn3NW7cON9iEz/++KP3eGFznL333nve/f7M13n33XfLsiyFhoYW2uukLO+Zvxo0aKD4+HhJxb/Hu3bt0qOPPqq2bduqRo0aCg8PV8OGDXXzzTdr/vz5xbZljNF7772niy++WDVq1FBUVJRat26tJ598UmlpaZKKnoftxPfmyJEjGjt2rNq0aaPY2FhZlqUpU6bkqzdnzhz169dPjRs3VkREhKKjo3XOOefo4Ycf1t69e/OVL81zU5TcBXsuueQS775LLrnE51wFxZ2cnKw77rhDzZo1U7Vq1VS9enW1atVK999/f5HzRuZdoCv3vJ9//rmuvvpq1a1bV3a7XV26dPEr9hMZY/TZZ5+pZ8+eatCggcLDw1WjRg0lJSVp7NixSk1NLbL+L7/8olGjRqlLly467bTTFBoaqujoaLVs2VJDhgzR2rVr/Y7lm2++Ub9+/dSkSRNFRkYqJiZGrVq1Uu/evTVjxgxv8qMwHo9Hb7/9tjp27KgaNWooMjJSZ599tp5++mllZGT4HUdp5D4TiYmJkqTU1FQ9/vjjatWqlSIjIxUbG6uLL75YU6dOLfZcM2fOVI8ePbw/G6tXr64mTZrooosu0mOPPaYlS5Z4y5b22Q70fQvm9UrS0aNH9dJLL+nSSy/VaaedprCwMNWtW1cdOnTQI488UuTPz0CfYX8dOHBAo0aN8v58Cg8PV2Jiom699VYtWLCgyLqJiYmyLEsDBw6UJK1fv1633367EhMTFRYWptq1a+v6668v9LOvPATyeeB2uzVlyhRdeeWV3ucpNjZWzZo102WXXaZnnnnG55nKnTN3zJgx3n2BLhy1fft279dNmzb1/8KLUJp7kvte5xozZky+68p9DgCgyjAAAJST+fPnG0lGknniiScKLXf8+HFvubPPPrvQck8++aSx2+3esie+LMsyjz/+eIF1t27d6i337rvvFhlrUa9///vfpa47f/58b50nnnjCuz+vtLQ0ExERYSSZgQMHFnovjDHG6XSa+Ph4I8lce+21Qb9n/sh7XwcMGFBk2dq1axtJJjo6utAy77zzjvf6C3sNHjzYOJ3OAutnZWWZbt26FVq3WbNmZtu2bUU+l3nfmz/++MMkJibmO0/eZyg9Pd1cf/31RcYcFRVlvvrqK592SvPcFGXAgAHFnuvEZ/+ZZ54xNput0PJhYWHmvffeK7C9vO/95MmTza233pqvfufOnf2KPa/9+/ebTp06FXkdtWvXNr/88kuB9d99991i70NISIiZMGFCkXEcPHjQXHbZZSW+p3nb//33382ll15aaN2kpCSTnp5e4nuUK+97vnXr1kKPN2rUyKxbt67AZzn3dc899xTYhsvlMjfddFOx96Fdu3beOqV5toPxvgXjenPNmTPH+/O1qFdBAn2G/fXdd9+Z6OjoItu55557jNvtLrB+o0aNjJTzs3vGjBmmWrVqhd736dOnBxTriTp37lzkPTQmsM+Do0ePmosuuqjY969nz57eOv48g4V9rxXmpZde8tZ7+eWXS3KLgnpPct/rol7FfYYDwKmGBCkAoNz4myBdvny5t9x1111XYJnHHnvMW6Zjx47mnXfeMYsXLzbLli0zU6dONRdccIH3+KuvvpqvfnEJ0jlz5pjIyEjTq1cv8+abb5off/zRrFixwsyePdu89NJLPv+5mDx5sk/d9PR0k5ycbJ566ilvme+++84kJyf7vPImQgpLkBpjzM0332yknETi8ePHC71vX3/9tfccBf3nNdB75g9/E6QHDhwwlmUZqfAk+KRJk7znOuuss8xrr71mFixYYFasWGFmzJhhrr76au/x4cOHF3iOO+64w1umZcuWZvLkyWbp0qXmhx9+MEOHDjUhISHm/PPPL/K5zPvenH322cbhcJh//etfZs6cOWbZsmVm2rRpZtGiRcaYnOTRJZdcYqScZPMtt9xiPv30U7Ns2TKzePFi88orr5iGDRsaSSY0NNQsW7bM205pnpui7Nq1yyQnJ5vJkyf7PKt5z3X48GFv+QkTJnjL1apVy7z44otm8eLFZsGCBWb06NEmMjLSe12zZs3K117e9/7ss882ksxFF11kPvroI7Ns2TIzd+5c88477/gVe957cuaZZ3rv15133mm++OILs2LFCvPzzz+bp59+2sTFxRlJpkaNGmbbtm35zjFx4kRTo0YNM2DAADN58mTz888/mxUrVpivv/7aPPnkk96kl2VZ5ocffigwjmPHjpnWrVt7r69du3bmrbfeMgsXLjTLli0zM2fONPfff7+pW7dukQnSjh07GpvNZgYMGGBmzZplli9fbmbOnOnzvVfQH1385W+CtFatWqZZs2amevXqZtSoUebHH380y5YtMxMnTjT169f3nmP27Nn5zvHaa695j1944YVmypQp5ueffzYrV640P/zwg3nllVfMVVddZZKSkrx1SvNsB+N9C8b1GmPMvHnzvH9YCgkJMQMHDjQzZ840y5cvNwsXLjQTJ040N9xwg3E4HPnqBuMZ9sfKlStNaGiokWQcDoe57777zPz5882SJUvMW2+9ZRo3buy9zocffrjAc+R+rrVp08aEh4ebxo0bm//+97/ml19+MYsXLzajR4824eHh3s+j/fv3lyrWghSXIA308+CBBx7wHu/WrZuZNm2aWbhwoVm+fLmZPXu2+c9//mMuuugic+ONN3rrHD582CQnJ5shQ4Z46574zCYnJ5vs7Gy/r3PevHnec8XHx5tVq1aV/GYF4Z5s2LDBJCcne48PGTIk33Xt2rWr1LEBQGVEghQAUG78TZDecsst3nLvv/9+vuNLlizx9nIbNWpUgedwu92mX79+RpKpXr26TyLImOITpAcOHMhXJ6+srCzzj3/8w0g5vZNcLle+MnkTI8X1MCkqQfrll196j3366aeFnqNPnz5GyumdmJGR4XMsGPfMH/4mSB999FFvuSeffDLf8R07dnh7Lw0YMKDQHqIjRowwkozNZjMbNmzwObZ8+XJvEjYpKckcO3YsX/1PP/3Up8dMcQlSm81mvv/++0Kv68UXX/QmKL755psCyxw6dMi0atXKm2A6UUmeG3/k/b4rrPfp/v37vfe7bt26ZseOHfnKrFixwpskrVevXr6kQN73XpLp37+/8Xg8AcU+dOhQI8nExMSYpUuXFlhm27Ztpk6dOkaS6devX77ju3btKvC9z5WamupN6Bb0fhhjzH333ee9rnvuuafQ68rKyjL79u3z2XdiL7QPPvggX73MzExz1llnGUkmLi6u0Oe9OP4mSCWZ2NhY8/vvv+crs3HjRm8SrKA/UOX2wuvQoUORcaakpOTbV5JnOxjvWzCuNyMjw/t8VatWrcge3AV93wTjGfZH+/btvQnc7777Lt/xQ4cOmZYtW3p/jhV0L/L+4a9du3YmNTU1X5kPP/zQW2bcuHGlirUgRSVIg/F50KBBAyPJJwFakIKe26I+n0vK7XZ7n9vcBH/nzp3N2LFjzXfffef3524w7okxxq/fyQCgqiBBCgAoN0UlSA8ePGh+/vln07VrV2+ZCy64oMCeGT179vT+B66oBMzhw4dNWFiYkWQmTpzoc6y4BKk/Vq1a5T1H3p6AuYKVIM3Ozvb2MOrRo0eB9dPT073Jq/79++c7Hox75o+iEqRHjhwxS5cu9SZhpZwh7ocOHcp3ntzePnXr1i2y16zT6TT16tUzkszIkSN9jt15553edlavXl3oOfIOhy8uQXrbbbcVep7s7GxvkuP+++8vtJwxxnzzzTfec27cuNHnWEUkSP/zn/94y0ybNq3Qc+XtAfjJJ5/4HMv73sfGxpq0tLSA4j5w4IA3cfXKK68UWfb111/3JqaLSqoV5v/+7/+8sR88eNDn2KFDh7yJiLZt2xb4x5Ci5H0/b7jhhkLLvfnmm349r0UpSYK0qF7ivXv3NlJOj8YTNWvWzK9nvCDBfraLet+MCc715n1fSjokurye4V9//dUb45133llouQULFnjL3X333fmO502QFvYMejweU7duXSPJXH/99SWKsyhFJUiD8XngcDj8eh8KEswEqTE5SfmmTZv6/OEkb8K0devW5uGHHy4woZkrGPfEGBKkAJAXizQBACrEiQsCxMfH66KLLtK3334ru92ufv36afbs2flW3XY6nfr2228lSTfeeGO+RY3yio2NVevWrSVJixcvDijerKws7dixQ2vXrtXvv/+u33//XcYY7/HVq1cHdP6iOBwO9erVS1LOAjEFLebxxRdf6NixY5Kkvn37+hyrqHuWd4Epy7IUExOj9u3b68MPP5RlWerWrZvmz5+vGjVqFHg9knTttdcqPDy80DbsdrsuuOCCAuP94YcfJEnnnnuuzj777ELP0b9/f7+v6cR7m9eSJUu8iy/lvl+Fufjii71fB3qfgyF3AbDY2Fj17Nmz0HL//Oc/89UpyLXXXqvq1asHFNN3332nzMxMSf7fT6fTqeXLlxdZ9tixY9q2bZvWrFnj/V7O+3PmxO/l+fPnexdPGjZsmEJCQkp8LbmKen7atWvn/XrLli2lbsMflmWpT58+xcZy+PDhfD9v6tSpI0n66quvdPDgwTKL8UQlfd/yCuR6Z82aJUmqVq2a7rjjjhLFXFbP8Inyfi8OHjy40HKdOnXSmWeema/OiVq3bl3oz0zLstSmTRtJZf+c5grG50Huc/vxxx+X+WJoxWnatKlWrVql5557TqeffrrPMWOMkpOT9fzzz6tly5Z64IEH5HK58p0jGPcEAOCLBCkA4KTTvHlzPfzww4qOjs53bO3atd7/3Dz66KMFriib97Vs2TJJ0r59+0ocx7Fjx/Tss8/qnHPOUWRkpBo1aqRWrVqpdevWat26tfc/iZLKPFGQm1jJzs7WZ599lu947irMtWvX1mWXXeZzrDzvmb/q1aunBx98UPXq1ct37MiRI9q0aZMk6a233io23tz7kTfezMxM7znyJp4Kct555/kdd1GJ1tz7JkkXXHBBkTFHRUV5y5blffbX77//Lklq06ZNvj9K5FW7dm3viuC5dQpS1H3yV977WadOnSLv51lnneUtW9D9PHjwoEaMGKEWLVqoevXqaty4sc466yzv9/I111zjUzavlStXer/Om9gujTPOOKPQYzVr1vR+ffTo0YDaKU58fLzi4uJKFcuAAQMkSZs2bVLTpk112223adq0adq1a1fQ4wzkfcsrkOvNff/PO+88VatWrUTxB/MZLkru92JoaKjP51JBOnToIEnauHGjsrOzCyxT1HMq/X2/yvo5lYLzeSD9/dwuWrRIjRs31tChQzVz5kwdOHCgzK+hIJGRkXrkkUe0adMmrV+/XpMnT9awYcOUlJQkmy3nv+hut1vjxo3z+cOUFLx7AgDwRYIUAFAhhgwZouTkZCUnJ2vlypWaNWuW7rzzTjkcDq1du1ZdunTRhg0b8tXbv39/qdoraY+Rbdu2qXXr1hoxYoR+++03ud3uIssfP368VHH5q2PHjt7EVG4yNNfBgwf1/fffS5J69+6dr4dbed2zE3Xv3t37Hq9evVqzZ8/Www8/rKioKO3atUtXXXWVfv7553z1ghFv3l5gCQkJRdarVauW320U1Ns1V0Xd52A4dOiQpJwEaHFOO+00nzoFKeo++StY93P58uU644wz9Oyzz+qPP/7w6fldkBO/l/Mm3nJ7oZVWUQm23KSIpGJ/3gSquERfUbHcdtttGjFihOx2u44cOaJ3331Xffr0UYMGDdS0aVM9+OCDQelZGOj7llcg15v7/pfmvS+vnwm534s1a9aU3W4vsmzu968xRocPHy6wjL/3q6yfUyl49/Cxxx7TbbfdJsuytH//fk2YMEE33HCDateurdatW+uJJ57Qn3/+GYyQS6xFixYaNGiQXnnlFf3666/asWOHT1L0vffe04IFC7zblfmzBgBOZkV/ggIAUEYSEhJ8esyce+65uvrqq3Xttdfquuuu06FDh9SnTx8tWbLEJ+GX9z9kL7zwgq666iq/2ouMjCxRfLfeequ2bt0qy7I0aNAg9e7dW2eeeaZq1aqlsLAwSZLH4/HGVtx/3gOVO0T0mWee0U8//aTdu3d7e19+8skn3iF4BQ3hLa97dqLY2Fif9/jss8/WlVdeqV69euniiy9WRkaG+vbtq99//92nt3DeeO+7774ih4zmFRoaWqo4i5py4ERFDa/OG/ePP/5YZI+1vIpL4JYnf+6FP896IMPQc+Xez9DQ0BINOa5fv7736+zsbPXq1UspKSlyOBz617/+pe7du6t58+aqUaOG93t5y5Yt3qGuZf29fCp4+umndccdd2jq1Kn64Ycf9MsvvygjI0ObN2/WSy+9pFdffVWvvvqq7rrrrlKd/2R830rycyJXMJ7hkgjW9+/JJFifBw6HQ5MmTdIDDzygadOmad68eVq2bJmys7O9UzaMGzdOH374obp37x7UayipevXqaeLEiUpPT9f06dMlSZ9++qkuvPBCSeX/GQkAVQUJUgDASeWaa67RXXfdpddff10rVqzQlClTfH75z5t0cjqdPgm4YFm/fr23t8ajjz6qp59+usByhfW+KSv9+vXTM888I4/Ho2nTpunBBx+U9HeP0mbNmql9+/b56pXHPSuJdu3aafTo0Xr44Ye1c+dOvfDCCxo7dqz3eN54MzIyShVvbGys9+vietuUtjfOifLGHRoaWuH3uSRq1qypvXv3+jUEM7eXVd5hyWUh935mZ2crLi6uVD345s2b5+3NOGHCBN1+++0Flivqezk+Pt779d69e9W4ceMSx3EqatSokUaMGKERI0bI6XRqyZIl+vTTT/XWW28pMzNTd999tzp06FDskO+CBON9C5b4+Hjt2rVLe/bsKXHdYDzD/sj9XkxJSZHL5SqyF2nu969lWUHp6V3WgvF5kFfLli01duxYjR07VsePH9fChQv10Ucf6f3331d6erpuueUWbd68uczeq5K4/fbbvQnS3CH1UvDvCQAgB0PsAQAnnSeeeMLbe3HMmDE+86S1atXK2wsid1h5sK1Zs8b7de/evQstl3d+uYKUpsdRUc4880xvsuGjjz6SlDMVQO7CC4UtAFMe96ykhg0b5u0l9fLLL/sMY65Vq5a3d+zcuXNL1eMpPDzc27OsuPepuOP+ypsICuQ+B/u58Ufuf7BXrlwpp9NZaLn9+/dr+/btPnXKSjDuZzC+l9u2bev9+qeffipVHKc6h8OhTp06afz48d6fTcaYfPMl+/tsB+tncDDkvv/Lli0r8RDlYP1MKE7u92J2drbPnLkFWbJkiaScP6hVhh6Fwfg8KExERIQuv/xyTZ48WS+88IKknKkavv76a59yFfEzWZLq1q3r/TrvNBBleU8AoCojQQoAOOkkJCTozjvvlCTt3LlT7733nvdYtWrVvIsQ/fjjj97/7AVT3hVji/oP8ZtvvlnkefKuLJuVlRV4YPo7Cbpy5UqtW7dOH330kfc/R4Wt0lwe96ykwsLC9PDDD0vKWQzr5Zdf9jl+3XXXScoZQlvQolT+yL3m1atX67fffiu03Pvvv1+q85/owgsv9PbkevPNN5WWllaq85TFc1Ocyy+/XFLO3K0zZswotNykSZO8z1tunbLStWtX74JRL7/8coErORfHn+9lj8ejt99+u9BzXHLJJd4/2Lz22mvlMu9iZZZ3kbgTF07y99kOxvsWLNdee603jpK2F4xn2B95vxcnTZpUaLnFixdr7dq1+eqc7ILxeVCcYDy3/ihJMjPvHwBO7LkerHuSe23l9VkDACczEqQAgJPSQw895P3F/bnnnvNJSowcOdLbo6N3797avHlzoedxu9366KOPSrTCcrNmzbxf503O5vXGG2/o//7v/4o8T94hekXFWBK33HKLtyfJ1KlTvb21kpKSfOI+UVnfs9K4/fbbvQuGTJgwQUeOHPEee+ihh7zzDN51113F9hT75ptv8iVB77jjDu8133777QUmWmbMmKGZM2cGdB25wsPDvdMe7Nu3T71799axY8cKLX/06FH997//zbe/LJ6b4gwaNMi7MMsDDzygnTt35iuzevVqPfPMM5Jy5sjr0aNHmcZUr149DRo0yNv2nXfeWWSCaf/+/XrnnXd89vnzvfzoo49qxYoVhZ43NjbW+web5cuX67777is0yeF0OoM2ZcPJ6sMPPyzyfcjbU/LEpI6/z3Yw3rdg6devn7e33siRI/W///2v0LIn/swMxjPsj6SkJO/0Ku+8847mzJmTr8yRI0e8z7HNZtOQIUNK3E5FCfTz4NChQ/ryyy+LTE4G47n1x8SJE3XHHXf4DJkvyPbt2zVy5Ejv9onzogbjM1L6+9rK67MGAE5qBgCAcjJ//nwjyUgyTzzxRLHl77nnHm/5999/3+fYE0884T0WFRVl7r33XjNr1iyzYsUKs3jxYjNt2jQzbNgwU7duXSPJJCcn+9TfunWrt/67777rc8zj8ZizzjrLe/yWW24xX3/9tVm+fLn5v//7P3PjjTcaSaZTp05FXk9aWpoJDw83kkzbtm3Nd999ZzZs2GA2btxoNm7caDIyMgq8nuJcdtllRpKJjY311nnllVeKrRfoPfNH3vs6YMCAYsu/8MIL3vJPPvmkz7F3333Xeyw0NNQMHjzYzJw50yxfvtz8+uuvZsaMGeaRRx4xp59+upFkvvrqq3znv+OOO7znaNmypXn33XfNsmXLzLx588y//vUvExISYpKSkrxlRo8eXeR9K47L5fK+P5JMw4YNzTPPPGPmz59vVq5caX766SczceJE07dvXxMZGWni4uLynaMkz40/8n7fzZ8/v9ByEyZM8JZLSEgw48aNM7/88otZuHChGTNmjImKijKSjGVZZtasWfnqF/U9VVpHjx71+V5s2bKlGT9+vPn555/NypUrzfz5881///tf06NHDxMaGmratWvnUz89Pd0kJCQYScZut5u7777bzJ492yxbtsxMnz7d+17l/V4uKPZjx46Z1q1be8u0a9fOvP3222bx4sVm+fLl5osvvjAPPfSQqVevXr76eZ/jrVu3Fnqtwbh/AwYMKLKt3OONGjUq8jxFxSzJ1K5d2wwZMsR88MEHZtGiRWbFihXm22+/NcOHDzcRERHenzE7d+70qevvsx2s9y0Y12uMMfPmzTN2u91IMiEhIWbgwIHmiy++MMuXLzeLFi0y7777rrnppptMaGhovrqBPsP+WrlypQkNDTWSjMPhMMOHDzfz5883S5cuNW+//bZp0qSJN4aHH364wHM0atTIr5/d/t7XkujcuXORP2cD+TzI/d5KTEw0w4cPNx9//LH55ZdfzLJly8xXX31l7rjjDmOz2YwkU79+fZOenu7T9saNG71tX3HFFeZ///uf+eOPP7zPrdPp9Ps6X3vtNe+5OnbsaMaMGWO+/vprs3TpUrN8+XLz5Zdfmvvvv99Ur17dW+66664L+j3J1bdvXyPJhIWFmTfffNMkJyd7r+vPP//0+7oA4FRAghQAUG5KmiDdsWOH9z98Z5xxhnG73T7HX375ZRMWFuY9Z2Gv0NBQs3HjRp+6xSUjVq5caWrUqFHoOVu3bm327NlT7PU8/PDDhZ4jb7KqJEm4yZMn+5wnJCTE7Nu3r9h6gd4zf5Q0QZqenm7i4+ONJBMXF2eOHj3qc3z69OkmOjq62HhtNpuZN29evvNnZWWZbt26FVqvcePGZtOmTd7t5557Lt85SvLeGGNMRkaG6d+/f7Ex57ZfEH+fG3/4myA1xpinn37amygo6BUWFmbee++9AuuWRYLUGGNSUlLMVVdd5df9vOSSS/LVnz17tjcpV9CrS5cu5vfffy829gMHDpiLL7642BiqQoK0uFdsbKz57rvvCjy3v892MN63YCVIc+Mp6jOhqJ8RgT7D/vruu++K/Xl5zz335PsszXUyJ0iNKf3nQd7vraJe9erVMytWrCiw7V69ehVar6jv6xN9/PHH3t9r/Hn16dOnyD+KBfoZuXLlykJ/J/DnMxwATiUMsQcAnLQaNGigAQMGSMpZWf7EuRHvu+8+bd68WY899pjOP/98xcfHy263KzIyUs2bN1fPnj315ptvavfu3WratGmJ2j733HO1atUq3XXXXWrUqJEcDodq1qyppKQkvfjii1qyZIlfq9w+99xzmjhxoi666CLVrFlTISEhJYqjID179vSZE+2yyy5T7dq1/apblvesNCIjI3X//fdLylmB+Y033vA5fvPNN2vbtm167rnn1KVLFyUkJMjhcKhatWpq0qSJrr32Wo0bN07btm3TJZdcku/8oaGh+vLLL/Xuu+/qwgsvVExMjKpVq6YzzzxTI0aM0PLly31WBI6JiQn4miIiIvTee+9p2bJlGjJkiFq1aqWYmBjZ7XbFxsbq3HPP1eDBg/XZZ59p3bp1BZ6jLJ4bf4wYMUIrV67U7bffrtNPP10RERGKjIzUmWeeqXvvvVfr169X//79yyWWXDVr1tS3336rH374QYMGDVKzZs0UFRUlu92umjVrqn379rrnnnv0zTffFDi0+Morr9SyZcvUr18/1a1bVw6HQ7Vq1VLnzp319ttv64cffvDOMVqU+Ph4/e9//9Pnn3+uG2+8UfXr11dYWJhq1Kihs846S3379tUXX3xR6FzAp4r169frtddeU48ePdSyZUvFxcXJbrerRo0aOv/88zV69Ght2LBBV1xxRYH1/X22g/W+BcuVV16pLVu26JlnnlHHjh0VFxcnh8OhevXqqUOHDhoxYoSSk5MLrBvoM+yvK664Qps2bdKIESN07rnnKjo6WmFhYWrYsKH69u2rn3/+Wf/97399FvypTEr7edCoUSOtWrVKL7zwgrp27aoWLVooNjZWdrtd8fHx6ty5s1588UWtW7fOZ2GtvD788EM9//zzSkpKUkxMTKnvYa9evbR//3598sknuueee3TBBReodu3aCg0NVWhoqOLj43X++efr/vvv17JlyzR16lRFREQE/Z7kOvfcc7V48WLdcsstatiwoXfYPgBURZYxLHsHAACqrgULFuiiiy6SlLMicN7FOgAAAACc+irnnw8BAACCZNq0aZIkh8Ohdu3aVXA0AAAAAMobCVIAAHDKOnjwoFJTUws9/t133+mtt96SJF133XWKjY0tn8AAAAAAnDQYYg8AAE5ZP/74o7p3766bbrpJl19+uU4//XTZbDZt375dX375pT788EO53W5FRERo1apVat68eUWHDAAAAKCckSAFAACnrB9//LHAhSnyio6O1qefflroojIAAAAATm0kSAEAwCkrPT1dM2bM0LfffqvffvtNBw4cUGpqqqKjo9W0aVNdddVVGjp0qGrVqlXRoQIAAACoICRIAQAAAAAAAFRZ9ooOAAXzeDzas2ePqlevLsuyKjocAAAAAAAAoFIxxujo0aOqW7eubLbC16onQXqS2rNnjxo0aFDRYQAAAAAAAACV2s6dO1W/fv1Cj5MgPUlVr15dUs4bGB0dXcHRAAAAAMCp69+zHtfB9IOSpFpRtfToZQ8qOry60jKP6tkfXtSB9AOSpPioWnrumjEVGSoAoATS0tLUoEEDb56tMMxBepJKS0tTTEyMjhw5QoIUAAAAAMrQL9uX6LUFb/rsi3BE6LjzuM++f114l85vlFSeoQEAAuBvfq3wwfcAAAAAAFQB5zdK0j+aX+qz78Tk6D+aX0pyFABOUQyxBwAAAABUeQPb99MZCc318arPtT99v3d/QlSCbj73BpKjAHAKq7QJ0q1bt2ru3LlasmSJlixZojVr1sjtdmvs2LEaNWpUic/n70rxU6ZM0YABA3y2Bw0aVGSdb7/9VldddVWJYwIAAAAAlJ/zGyXp/EZJcnlcOpadocjQarLbKu1/mwEAfqq0P+lfeeUVvfLKK0E7X6dOnQo9dvjwYa1du1aSdP755xdYJiEhQc2aNSvwWI0aNQIPEAAAAABQLuw2u2LCWQsCAKqKSpsgjY+PV7du3ZSUlKT27dvrnXfe0YwZM0p9vgULFhR6bNSoUVq7dq2SkpLUokWLAst07dpVU6ZMKXX7AAAAAAAAAMpfpU2QnjiMfvr06WXSjjFGU6dOlSTdeuutZdIGAAAAAAAAgIrBKvbF+Pnnn7Vt2zY5HA717t27osMBAAAAAAAAEESVtgdpefnwww8lSVdddZXi4+MLLbd69Wr16dNH+/btU3R0tNq0aaN+/frp9NNPL69QAQAAAAAAAJQQCdIiZGVl6dNPP5VU/PD6VatWadWqVd7tL774QmPHjtWYMWM0cuRIv9rKysrybqelpZUuaAAAAAAAAAB+I0FahK+++kqpqamKiYnRtddeW2CZ2NhY/etf/1Lv3r3VtGlTxcTEaN26dRo3bpw++OADjRo1SjExMRo6dGiRbT377LMaM2ZMvv1r1qxRVFRUUK4HAAAAAAAAqCrS09P9KkeCtAi5w+tvuukmhYeHF1imR48e6tGjh8++c889V++//77i4uI0fvx4jRo1SgMGDFD16tULbevRRx/V8OHDvdtpaWlq0KCBWrVqpejo6MAvBgAAAAAAAKhC/B2hzSJNhUhJSdE333wjSerfv3+pzjFmzBiFhYXpyJEjmjdvXpFlw8LCFB0d7fMCAAAAAJQ/j/Eo05Ulj/FUdCgAgHJAD9JCfPzxx3I6nUpMTNSFF15YqnNER0erVatWWrFihTZt2hTkCAEAAAAAwbT98A59u+57/bpjmbLd2QoNCVWHhuep65lXqFGNhhUdHgCgjJAgLUTu8Pp+/frJsqxSn8fhcEiSXC5XUOICAAAAAATfom2/6I1F7/j0Gs12Z+vnrYu0cNsvGtLxn+qYeH4FRggAKCsMsS/A5s2btXjxYkk5CdLScrvd2rBhgySpfv36QYkNAAAAABBc2w/v0BuLJhY6pN5jPHpj0URtP7yjnCMDAJQHEqQF+OCDDyRJSUlJatGiRanPM2nSJKWmpiokJERdunQJUnQAAAAAgGD6dPVMeYzxbkc4InRmQgtF2P9erNdjjD5bPbMiwgMAlLEqlSAdP368EhMT1bt37yLLTZ06VZJ06623FlkuLS1Nt9xyi5YsWeKz3+12a+LEibr33nslSYMHD1a9evUCiBwAAAAAUBY8xqPVe5K9250Sz9eEG8Zp1D8e0YSeL6tTnmH1q/Yks3ATAJyCKu0cpAsXLlT37t292+np6ZKkZ599VuPHj/fuX7lypRo0aCBJSk1N1fbt25WYmFjoeRcvXqxNmzbJ4XAUm0j1eDyaPn26pk+frtjYWDVu3Fh2u10bN25UamqqJKlr16565ZVXSneRAAAAAIAylXe1eptl0z87DFSoPVSSFGYP0+AOA7R4+xJ5jMe7un01R0RFhgwACLJKmyB1Op1KSUnJtz8jI0MZGRnebbfbXaLz5g6vv+qqqxQfH19k2cjISD3//PNatGiRfv/9d23evFnHjx9XXFycrrnmGvXv31833XRTQIs8AQAAAADKj5GRx3iU7XYqNMRR0eEAAMqBZUyeiVZw0khLS1NMTIyOHDmi6Ojoig4HAAAAAE5JHuPRgGl3eHuR1oqMV2pGqpzGJYdlV2y1WB04dlBSTg/T9255WzarSs1WBwCVlr/5NX6qAwAAAACqLJtl0zl1Wnu3Dxw7KKdxSZKcxuVNjkrSOXXPJjkKAKcgfrIDAAAAAKq0C5tc4F+5xucXXwgAUOmQIAUAAAAAVGkLtiz2r9zWX8o4EgBARSBBCgAAAACosjzGo9V7k73bYfYwn+N5t1fv+c07VykA4NRBghQAAAAAUGVlurJ8kp5Zriyf43m3PcajzBOOAwAqPxKkAAAAAADkEeGIUItazRRhD6/oUAAA5YAEKQAAAACgygo/cUh9SJjcHrc2HNgot/EoLCSsyPIAgMrPXtEBAAAAAABQkSxZMjKSpCz330Pos93Z+coBAE499CAFAAAAAFRZ2W6nNzlaHCOjbLezjCMCAJQ3EqQAAAAAgCorNMQhm+Xff41tlk2hIY4yjggAUN5IkAIAAAAA8JeEyHiFhoRKkkJDQpUQGV/BEQEAyhoJUgAAAABAlZXtdspjPN7t/ccOKsQWoua1mirEsmn/sYPeYx7jYYg9AJyCWKQJAAAAAFBlhYY4FBYSqqw8CzIddx7XHwc25SsbFhLKEHsAOAXRgxQAAAAAUGXZLJuSGp7n3a4dlaBQW04SNNTmUO2oBO+xpEbt/Z6vFABQefCTHQAAAABQpXU98wqFWCGSpD/T98se4lDzWs1kD7Hrz/T9kqQQK0Rdz/hHRYYJACgjJEgBAAAAAFVaoxoNdVfHwd4kaYYzQ38c2KgM53FJOcnRuzoOVqMaDSsyTABAGWEOUgAAAABAldcx8XzVi6mrb9fP0ZLtS5XlzlZYSKiSGrVX1zP+QXIUAE5hljHGVHQQyC8tLU0xMTE6cuSIoqOjKzocAAAAAKgyclerDw1xMOcoAFRi/ubX6EEKAAAAAEAeNsumcHtYRYcBACgn/CkMAAAAAAAAQJVFghQAAAAAAABAlUWCFAAAAAAAAECVRYIUAAAAAIA8PMajTFeWPMZT0aEAAMoBizQBAAAAACBp++Ed+nbd91qyY5my3NkKCwlVUsPz1PXMK9SoRsOKDg8AUEZIkAIAAAAAqrxF237Rm4smyW3c3n1Z7mz9vHWRFm37VXd1HKyOiedXYIQAgLLCEHsAAAAAQJW2/fAOn+RotdBqOjOhhao5IiRJbuPWm4smafvhHRUZJgCgjNCDFAAAAABQpX277ntvcvTiJp3U/7w+siybjPHo/WUf6actC+U2bn27fo7uumBwBUcLAAg2EqQAAAAAgCrLYzxasmOZJCncHi6Xx617ZtzvnYO0XYM2CreHK9OVqSXbl+qO8wfJZjEYEwBOJSRIAQAAAABVVrbbqSx3tiQp05WpRdt+8R7Lcmdr0bZffbaz3U6F28PKPU4AQNnhz14AAAAAgCorNMQhh82/vkMOm12hIY4yjggAUN5IkAIAAAAAqiybZVONajX8KlujWk2G1wPAKYif7AAAAACAKstjPDqUcTjf/oi/VrDP61DGIXmMpzzCAgCUIxKkAAAAAIAqK9vtlMvjyrf/uPN4vn0uj0vZbmd5hAUAKEckSAEAAAAAVZbdFuKzHV8trsjtE8sDACo/EqQAAAAAgCrL5XH7bB/MSCly+8TyAIDKjwQpAAAAAKDKCg1xyF7AKvYFzUFqZxV7ADglkSAFAAAAAFRZNsummgWsYl/QHKQ1WcUeAE5J/GQHAAAAAFRZHuPR4QJWsS/IYVaxB4BTEglSAAAAAECVle12ylnAKvYFcbKKPQCckkiQAgAAAACqrNAQh8JCQv0qGxYSyhykAHAKIkEKAAAAAKiybJZNLU8706+yrU47kzlIAeAUxE92AAAAAECVllHAgkzVCljFvqByAIDKjwQpAAAAAKDK8hiPNh7YlG9/QcnQPw5sYpEmADgFkSAFAAAAAFRZma4sn6Rnrch4n+N5tz3Go0xXVrnFBgAoH5U2Qbp161ZNnDhRt99+u8455xzZ7XZZlqWnnnqqVOcbPXq0LMsq8rV+/fpC669bt059+/ZVnTp1FB4ertNPP10PPvigUlNTS3mFAAAAAIDyduDYwSK3AQCnHntFB1Bar7zyil555ZWgn7dBgwZq2LBhgceqVatW4P758+frmmuu0fHjx1WrVi21atVK69ev10svvaSZM2dq0aJFql27dtBjBQAAAAAEJtweJsuyZIwptqxlWQq3h5VDVACA8lRpE6Tx8fHq1q2bkpKS1L59e73zzjuaMWNGwOe97bbbNHr0aL/LHz16VDfffLOOHz+uYcOG6cUXX5TD4VBKSoq6d++uhQsXavDgwfr6668Djg0AAAAAEFw2y6bY8FgdPn642LKx4bGsYg8Ap6BKmyAdNWqUz/b06dMrJI4333xTBw4c0Jlnnqlx48YpJCREkhQXF6ePPvpIp59+umbNmqUVK1aobdu2FRIjAAAAAKBgHuPR0cw0v8oezUyTx3hIkgLAKYaf6gH6/PPPJUkDBw70JkdzNWzYUJdffrkk6bPPPiv32AAAAAAARct2O+Uybr/Kuoxb2W5nGUcEAChvlbYHaVmZP3++1qxZo5SUFNWsWVNJSUnq37+/TjvttHxlXS6Xli9fLknq1KlTgefr1KmTZs+erV9//bVM4wYAAAAAlJzdFlJ8oQDKAwBOfiRIT/DTTz/5bM+YMUOjR4/W66+/roEDB/oc27Ztm5zOnL8eNmnSpMDz5e7fuHFj8IMFAAAAAATE5fGv92je8nYb/5UGgFMJQ+z/UqdOHY0YMUJLly5VSkqKMjIytHDhQnXt2lXHjx/Xbbfdpq+++sqnzuHDf0/iXaNGjQLPm7s/b9mCZGVlKS0tzecFAAAAAChbhfUIDbeHl6g8AKDy4s9ef7nzzjvz7evYsaNmzZqlnj17aubMmbr//vvVrVs3WZYlScrMzPSWDQ0NLfC8YWFhkqTjx48X2f6zzz6rMWPG5Nu/Zs0aRUVF+X0dAAAAAAD/ZXsKnlM005VZ4P7Vyb8p1OYoy5AAAEGSnp7uVzkSpMWwLEvPPfecZs6cqc2bN+u3337TOeecI0kKD//7L4rZ2dk+27mysrIkSREREUW28+ijj2r48OHe7bS0NDVo0ECtWrVSdHR0MC4FAAAAAHACl8cl/e5/+XNan80QewCoJPwdoc0Qez80b95cNWvWlCRt2rTJuz/vsPrChtDn7i9sCH6usLAwRUdH+7wAAAAAAGWrNHOQAgBOLSRI/eRw5AyhcLlc3n2JiYne/Vu2bCmwXu7+Zs2alXGEAAAAAICSCg0p2XD5kpYHAJz8SJD64eDBg9q/f78kqX79+t79drtdbdu2lSQtXLiwwLq5+zt06FDGUQIAAAAASspjPGVaHgBw8iNB6odx48bJGKOYmBi1b9/e59gNN9wgSZoyZYrcbt+hFjt27NDcuXMlST179iyfYAEAAAAAfjuWnVGm5QEAJ78qlSAdP368EhMT1bt3b5/9a9as0d133601a9b47M/MzNQzzzyj//znP5KkRx55JN9q9XfddZfi4+O1bt06DR8+XE5nzgqIKSkp6tOnj1wul7p27ap27dqV4ZUBAAAAAEojMrRamZYHAJz8Km2CdOHChYqPj/e+pk+fLkl69tlnffbv3LnTWyc1NVXbt2/Xvn37fM7ldDr1xhtv6KyzzlJCQoLOO+88nXfeeYqLi9PIkSPl8Xg0ePBg/fvf/84XR3R0tKZPn67w8HC9+uqrqlevns477zw1bNhQCxcuVGJioiZPnly2NwMAAAAAUCo2q2T/LS5peQDAya/S/mR3Op1KSUnxvrKysiRJGRkZPvtPHPZekMTERI0dO1Zdu3ZVVFSUNmzYoOTkZNWsWVM33nijZs+erXfeeUeWZRVY/7LLLtOyZcvUu3dvWZal5ORk1a5dW8OHD9eKFSt02mmnBfXaAQAAAADBke12lml5AMDJzzLGmIoOAvmlpaUpJiZGR44cUXR0dEWHAwAAAACnJJfHpQHT7vC7/Hu3vC27zV6GEQEAgsXf/Bo/1QEAAAAAQeMxHqVnpVd0GH7LcmX5bNssm89K9SduH844rDB7WLnFV1pRYVFMBwAAfiJBCgAAAAAImvSsdA2ZcV9Fh1FqeZOhBW3f98Uj5RlOqb3Rc7yiwxmNCAD+4M9JAAAAAAAAAKosEqQAAAAAAAAAqiwSpAAAAAAAAACqLOYgBQAAAAAETVRYlN7oOb6iwyiRXam79ey8l/LNN5qXzbLp0UsfUP3YeuUYWelFhUVVdAgAUGmQIAUAAAAABI3NslW6xYFanhatIR3/qTcXTZLbuPMdD7FCdFfHwWp52pkVEB0AoKyRIAUAAAAAVHkdE89XvZi6+nb9HP28ZaF3/0VNOqnrGf9QoxoNKzA6AEBZYg5SAAAAAAAkNarRUHddMFix4bGSpNjwWN11wWCSowBwiiNBCgAAAABAHpZl+fwLADi1kSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAAAAAFBlkSAFAAAAAAAAUGWRIAUAAAAAAABQZZEgBQAAAErBY4yyXR55jKnoUAAAABAAe0UHAAAAAFQme1KztOCPI0redUxOt5EjxFLr+pG6sHmM6saGVXR4AAAAKKGAEqQ//fSTJKl9+/aKiIjwq05mZqaWLFkiSbr44osDaR4AAAAoV6t2pOuTJfvlydNp1Ok2WrE9Xat2pKtXUoLObRhVcQECAACgxAJKkHbp0kU2m02//fabWrZs6Ved3bt3e+u5XK5AmgcAAADKzZ7ULJ/kaLjDpjqxodqbmq1Mp0ceI32yZL8Soh30JAUAAKhEAh5ib0o551Jp6wEAAAAVYcEfR7zJ0XaJUereJl6hdpuyXR59sfKglm9Ll8fklOuVlFCxwQIAAMBv5b5Ik8fjkSSFhISUd9MAAABAqXiMUfKuY5Jyeo72aJuTHJWkULtN3dvEK9yRs5286xgLNwEAAFQi5Z4g3bZtmyQpJiYmoPNs3bpVEydO1O23365zzjlHdrtdlmXpqaeeKtX5Vq5cqccff1ydO3dWfHy8HA6HEhIS1LVrV82cObPQelOmTJFlWUW+Zs+eXdrLBAAAwEnA5TZyunOSnnViQ+UI8f01OtRuU52YUEk5c5K63CRIAQAAKosSDbHfsWNHgfv37t2rqKiiJ6PPysrS5s2b9dhjj8myLLVq1aokTefzyiuv6JVXXgnoHLk2b96stm3bercbN26sxMREbdmyRbNnz9bs2bM1YMAATZ48WTZbwTnlhIQENWvWrMBjNWrUCEqcAAAAqBj2EEuOEEtOt9He1Gxluzyyh1hyuY33371HsiVJjhBL9hCrgiMGAACAv0qUIG3cuHG+fcYYXXHFFSVuuH///iWuk1d8fLy6deumpKQktW/fXu+8845mzJhRqnMZY1SnTh3dd999uvXWW1WnTh1JOdMBvP766xo2bJjee+89nXfeeRo6dGiB5+jataumTJlS2ssBAADAScxmWWpdP1Irtqcr0+nR+O936WimW063kSPEUvXwEGU6c6aSal0/UjaLBCkAAEBlUaIEaWELK5VkwaXw8HANGzZMt912W0mazmfUqFE+29OnTy/1uerXr69NmzapWrVqPvttNpuGDh2qNWvW6M0339TEiRMLTZACAADg1HZh8xit3J4uI+nQMZd3v9NtvNvWX+UAAABQeZQoQfruu+/6bA8aNEiWZWns2LGqV69eofUsy1J4eLjq1KmjNm3aFDscv7yFh4cXefyKK67Qm2++qT/++KOcIgIAAMBJyZJUVN8AOo4CAABUOiVKkA4YMMBne9CgQZKkHj16qGXLlsGL6iSTmZkpSYqIiCi0zOrVq9WnTx/t27dP0dHRatOmjfr166fTTz+9vMIEAABAGVrwxxHlDpyqGWlX2nGXXB7JbpOiI+w6dMwlY3LK9UpKqNhgAQAA4LcSJUhPNH/+fEkFz016Kvnkk08kSZ06dSq0zKpVq7Rq1Srv9hdffKGxY8dqzJgxGjlyZLFtZGVlKSsry7udlpZW+oABAAAQVB5jlLzrmCQp3GHT/VfWV4jNd5GmZ77eoUynR8m7junG9oZ5SAEAACqJgBKknTt3DlYcJ63vv/9e//d//ydJeuihh/Idj42N1b/+9S/17t1bTZs2VUxMjNatW6dx48bpgw8+0KhRoxQTE1Ps3KXPPvusxowZk2//mjVrTropCQAAAKoal0dyuiMlSdF2p9avXZOvTLQ9XJnOEDndRqt/+112W3lHCSBYXC6n99/k5OQKjgYAUFrp6el+lQsoQXqiLVu2aPHixdq3b58yMjI0ZMgQxcfHB7OJcrVjxw717dtXknT33Xfr4osvzlemR48e6tGjh8++c889V++//77i4uI0fvx4jRo1SgMGDFD16tULbevRRx/V8OHDvdtpaWlq0KCBWrVqpejo6OBcEAAAAErFY4w+37xNTrdRmsuhFmeertA8GdBsl0czNu+Q5JEjxNI5Z59FD1KgErNvdEhOyW53qHXr1hUdDgCglPwdoR2Uv2uvXLlSnTt3VrNmzdS/f389/PDDGj16tPbv3+9TbsKECUpISFCzZs3kdDqD0XSZOXTokLp27aqDBw+qS5cuGjduXInPMWbMGIWFhenIkSOaN29ekWXDwsIUHR3t8wIAAMDJwWZZal0/pwdpptOjL1YeVLbLIyknOfrFyoPKdOZst64fSXIUAACgEgk4QTpr1ix17NhRCxYskDHG+yrIgAEDdPz4cW3ZskVff/11oE2XmfT0dF199dVau3at2rVrpy+//FJhYWElPk90dLRatWolSdq0aVOwwwQAAEA5urB5jGx/5T2Xb0vXM1/v0Fvz9+iZr3do+bac4Vs2K6ccAAAAKo+AEqT79u3TLbfcoqysLLVs2VLffvutjh49Wmj5qKgo73D0b7/9NpCmy0xWVpa6d++uX3/9VS1bttTs2bOLHBpfHIfDIUlyuVzBChEAAAAVoG5smHolJXiTpJlOj7YezPT2HLVZUq+kBNWNLfkf1gEAAFBxApqD9OWXX1Z6eroaNWqkn3/+WbGxscXW6dKli6ZOnarly5cH0nSZcLlc6tWrl+bNm6cmTZpozpw5Ac2h6na7tWHDBklS/fr1gxUmAAAAKsi5DaOUEO3Qgj+OKHnXMTndRo6QnOH3FzaPITkKAABQCQWUIP3uu+9kWZYeeOABv5KjktSiRQtJ0rZt2wJpOuiMMRo4cKC+/PJL1a1bV3PnzlXdunUDOuekSZOUmpqqkJAQdenSJTiBAgAAoELl9iS9sb2Ry21kD7GYcxQAAKASC2iI/datWyVJSUlJftfJHa6enp4eSNOlMn78eCUmJqp37975jt17772aOnWq4uPjNXfuXDVu3LjY86WlpemWW27RkiVLfPa73W5NnDhR9957ryRp8ODBqlevXnAuAgAAACcFm2Up1G4jOQoAAFDJBdSDNHcl+tx5Nv2RmpoqSYqMjAykaS1cuFDdu3f3bucmXJ999lmNHz/eu3/lypVq0KCBt+3t27crMTHR51yLFy/Wa6+9JkmKiIjQ7bffXmi7CxYs8H7t8Xg0ffp0TZ8+XbGxsWrcuLHsdrs2btzovc6uXbvqlVdeCeRSAQAAAAAAAJSRgBKkp512mrZv366tW7eqTZs2ftVZvHixpMDn5HQ6nUpJScm3PyMjQxkZGd5tt9td7LmysrK8X+/cuVM7d+70K4bIyEg9//zzWrRokX7//Xdt3rxZx48fV1xcnK655hr1799fN910kyx6FQAAAAAAAAAnpYASpJ06ddL27ds1c+ZM3XDDDcWWz8jI0JtvvinLsnTxxRcH0rS6dOkiY0yJ6owePVqjR48OyrmknJ6zDz30UInrAQAAAAAAADg5BDQH6YABA2SM0bRp0/T9998XWTY9PV29evXSjh07JOXMywkAAAAAAAAAFSmgBOnll1+uHj16yOPx6LrrrtNDDz3ks2DRoUOH9Ouvv2rs2LFq0aKFvv32W1mWpf79+/s9JB8AAAAAAAAAyoplSjO2PI+MjAx169ZNP/74Y5FzbeY2c9lll+nrr79WWFhYIM2e8tLS0hQTE6MjR44oOjq6osMBAAAAgCpj6OcP6PDxw6oRUUP/veGlig4HAFBK/ubXAupBKknVqlXT3Llz9cILL+i0006TMabAV82aNfXMM8/ou+++IzkKAAAAAAAA4KQQ0CJNuWw2mx544AHde++9WrJkiZYtW6b9+/fL7XYrLi5Obdq00YUXXkhiFAAAAAAAAMBJJaAE6fvvvy9JatGihTp06CC73a6OHTuqY8eOQQkOAAAAAAAAAMpSQEPsBw4cqEGDBmn79u3BigcAAAAAAAAAyk1ACdKYmBhJUrNmzYISDAAAAAAAAACUp4ASpI0bN5YkHT58OCjBAAAAAAAAAEB5CihBev3118sYo6+++ipY8QAAAAAAAABAuQkoQXrvvfeqUaNGeuONNzRv3rxgxQQAAAAAAAAA5SKgBGl0dLTmzJmjM844Q1deeaXuuOMO/fjjjzp06JCMMcGKEQAAAAAAAADKhD2QyiEhId6vjTGaNGmSJk2a5Fddy7LkcrkCaR4AAAAAAAAAAhJQgvTEXqL0GgUAAAAAAABQmQSUIH3iiSeCFQcAAAAAAAAAlDsSpAAAAAAAAACqrIAWaQIAAAAAAACAyowEKQAAAAAAAIAqiwQpAAAAAAAAgCoroDlIn3zyyRLXsSxL4eHhiomJUbNmzdSuXTtFR0cHEgYAAAAAAAAAlEpACdLRo0fLsqyAAnA4HOrevbuefvppNW3aNKBzAQAAAAAAAEBJBDzE3hgjY4zP14W9CiqTnZ2tzz77TOeee65++OGHQMMBAAAAAAAAAL8FlCD1eDzatm2bzj//fBljdP3112vmzJnauXOnMjMzlZWVpZ07d2rmzJnq0aOHjDHq0KGDNm/erMOHD+vnn3/WkCFDZLPZlJGRoRtvvFEpKSnBujYAAAAAAAAAKFJACdKjR4/qiiuu0LJly/Tpp59qxowZ6t69u+rVq6fQ0FA5HA7Vq1dP3bt31+eff65PP/1Uy5Yt0xVXXCFJ6tSpkyZMmKCvv/5aISEhSktL04QJE4JyYQAAAAAAAABQnIASpOPHj9cff/yhIUOGqGfPnsWW79mzp4YMGaLNmzfrpZde8u6/8sor1bdvXxlj9O233wYSEgAAAAAAAAD4LaAE6aeffirLsnT99df7XeeGG26QJH3++ec++7t37y5J2rRpUyAhAQAAAAAAAIDfAkqQbt26VZIUHR3td53cstu3b/fZ36hRI0lSWlpaICEBAAAAAAAAgN8CSpA6HA5JUnJyst91csvm1s3l8XgkSbGxsYGEBAAAAAAAAAB+CyhB2rp1axlj9OKLLyozM7PY8sePH9cLL7wgy7LUunVrn2ObN2+WJNWqVSuQkAAAAAAAAADAbwElSG+77TZJ0tq1a3XppZdqzZo1hZb9/fffdemll2rt2rWSpMGDB/scnzt3rizL0tlnnx1ISAAAAAAAAADgN3sglQcMGKDPPvtMs2bN0q+//qqzzz5b5557rtq1a6eEhARJ0v79+7Vs2TKtXr3aW69bt27q37+/d/vIkSP69NNPZYxR165dAwkJAAAAAAAAAPwWUIJUylmNfujQoXrnnXdkjNGqVau0atWqfOWMMbIsS3fccYdee+01n2Mul0tffPGFJOm8884LNCQAAAAAAAAA8EvACVKHw6G33npLd911l95++2398MMP2rRpk0+Z008/XZdddpnuuOMOtW3bNt854uLi1Llz50BDAQAAAAAAAIASCThBmqtNmzZ64403JElZWVlKTU2VlLMqfVhYWLCaAQAAAAAAAICgCVqCNK+wsDDVrl27LE4NAAAAAAAAAEET0Cr2AAAAAAAAAFCZBa0H6aZNm/T+++9r8eLF2rdvn44fP67Zs2eradOm3jK///67duzYocjISOYcBQAAAAAAAFDhAk6QejwePfLIIxo/frw8Ho+MMZIky7KUnZ3tU3bnzp3q1q2b7Ha7tm7dqnr16gXaPAAAAAAAAACUWsBD7O+8806NGzdObrdbdevW1Y033lho2a5du6pJkyZyu9367LPPAm0aAAAAAAAAAAISUIL0xx9/1KRJkyRJI0aM0LZt2/TJJ58UWeemm26SMUbz588PpGkAAAAAAAAACFhAQ+zffPNNSdLVV1+tp556yq86SUlJkqQ1a9YE0jQAAAAAAAAABCygHqSLFy+WZVkaPHiw33Xq168vSdq3b18gTQMAAAAVymOMsl0eef6agx8AAACVU0A9SPfv3y9Jaty4sf8N2nOadDqdgTQNAAAAVIg9qVla8McRJe86JqfbyBFiqXX9SF3YPEZ1Y8MqOjwAAACUUEAJ0oiICGVnZysjI8PvOjt27JAk1ahRI5CmAQAAgHK3ake6PlmyX548nUadbqMV29O1ake6eiUl6NyGURUXIAAAAEosoCH2uT1HV65c6Xedr7/+WpLUsmXLQJrW1q1bNXHiRN1+++0655xzZLfbZVmW33OhFmbx4sXq3r27atWqpYiICLVs2VJjx45VZmZmkfXWrVunvn37qk6dOgoPD9fpp5+uBx98UKmpqQHFAwAAgJPDntQsn+RouMOmxrXCFe7I+ZXaY6RPluzXntSsCowSAAAAJRVQgvSKK66QMUZvv/22PB5PseWXL1+uDz74QJZl6aqrrgqkab3yyiu644479M477+i3336T2+0O6HySNHXqVF100UX68ssvFRYWpjPPPFObNm3S448/rosvvrjQnrLz589Xu3bt9NFHH8ntdqtVq1bat2+fXnrpJbVr105//vlnwLEBAACgYi3444g3OdouMUojujXUnV3qakS3hmqXmNNr1GNyygEAAKDyCChBOnToUEVERCg5OVm33357kfOKzpgxQ1dddZWys7MVHR2tO+64I5CmFR8fr27duunJJ5/Ut99+q549ewZ0vm3btmnw4MFyu916/vnntXPnTq1YsUIbN25UixYttHTpUj388MP56h09elQ333yzjh8/rmHDhmn37t1avny5duzYoU6dOmnLli0lWsQKAAAAJx+PMUredUxSTs/RHm3jFWrP+VU61G5T9zbx3p6kybuOsXATAABAJRJQgrRevXp69dVXZYzRlClT1KRJE919993e45MmTdKQIUPUrFkz9erVSykpKbIsS2+//bZiYmICCnzUqFH66quv9Nhjj+mqq65SVFRgcz298MILysrK0hVXXKGHHnpIlmVJkho1aqTJkydLkt5+++18vUHffPNNHThwQGeeeabGjRsnh8MhSYqLi9NHH30ku92uWbNmacWKFQHFBwAAgIrjchs53TlJzzqxoXKE+P4aHWq3qU5MqKScOUldbhKkAAAAlUVACVJJGjx4sN555x1FRERo9+7deuutt7zJxfHjx+vtt9/W5s2bZYxRWFiYJk+erJtuuingwIPJGKOZM2dKUoG9PTt27KgzzjhDTqdTX3zxhc+xzz//XJI0cOBAhYSE+Bxr2LChLr/8cknSZ599VhahAwAAoBzYQyw5QnJ+x92bmq1sl+/0Utkuj/YeyZYkOUIs2f8qCwAAgJNfwAlSSbrtttu0fv16DR8+XKeffrqMMT6vevXqaciQIVq3bp0GDBgQjCaDaseOHdq7d68kqVOnTgWWyd3/66+/eve5XC4tX768xPUAAABQudgsS63rR0qSMp0efbHyoDdJmu3K2c505my3rh8pm0WCFAAAoLKwB+tE9evX14svvqgXX3xRaWlp2r9/v9xut+Li4hQfHx+sZsrExo0bJUlhYWGqW7dugWWaNGniU1bKmbc0d97V3OP+1AMAAEDlc2HzGK3akS6PkZZvS9ea3RmqExOqvUeyvclRm5VTDgAAAJVHUHqQnig6OlpNmzZVixYtCkyOZmRk6KWXXiqLpkvl8OHDkqTY2Fjv9AAnqlGjhk/ZE7/OPe5PvYJkZWUpLS3N5wUAAICTR93YMPVKSpDtr18XM50ebT2Y6ZMc7ZWUoLqxYRUYJQAAAEoqaD1I/XH06FG9+uqreuWVV5SSkqIHHnigPJsvVGZmpiQpNDS00DJhYTm/6B4/fjxfvaLqFlSvIM8++6zGjBmTb/+aNWsCXoAKAAAAwREi6apGNq0/ZNf2o3a5jaUQy6hRdZfOqOlSyJGtSk6u6CgBBMrlcnr/TeabGgAqrfT0dL/KlUuC9NChQ3r55Zc1YcIEHTlyRMaYQntqVoTw8HBJUnZ2dqFlsrKyJEkRERH56uXWzbtdVL2CPProoxo+fLh3Oy0tTQ0aNFCrVq0UHR3tx1UAAACgvFwsyWNyVqu3h1jMOQqcYuwbHZJTstsdat26dUWHAwAoJX9HaJc4Qbpz5069+OKLmjNnjnbu3Cm73a7ExETddNNNuueeexQT8/ecS+np6XrhhRf08ssv69ixYzLGSJLi4+N1//33l7TpMpM7DD41NbXQ5G3uEPm8Q+nzfn348GHVqVPHr3oFCQsL8/Y2BQAAwMnPZlkKtZMYBQAAqOxKlCD94YcfdMMNN3i7p+YmPH/77Tf99ttv+uCDDzRv3jzVqVNHCxcuVN++fbVz505vudNOO00PPPCAhgwZomrVqgX5UkqvWbNmknJ6e+7Zs0f16tXLV2bLli0+ZSUpMTFRDodDTqdTW7ZsKTBBWlA9AAAAAAAAACcHvxdpSklJ0S233KKjR4/KGCNjjOLj41W7dm1JOcnSP/74Q0OGDNHPP/+sf/zjH97kaP369fXqq69q69ateuCBB06q5KgkNWzYUKeddpokaeHChQWWyd3foUMH7z673a62bduWuB4AAAAAAACAk4PfCdJJkybp4MGDsixL/fv3144dO7R//37t3btX+/fv17///W8ZY/T111+rX79+yszMVEREhJ5//nlt2rRJQ4cOPWmHkFuWpeuvv15SznWeaNGiRVq/fr0cDoeuu+46n2M33HCDJGnKlClyu90+x3bs2KG5c+dKknr27FkWoQMAAAAAAAAIgN8J0u+++06SdN5552nKlCmqX7++91hcXJyeeeYZ9e3bVx6PRzt37lT16tX1008/6cEHHyxydfjyNH78eCUmJqp37975jj300EMKDQ3V999/rxdeeME7LcD27dt12223SZL++c9/enua5rrrrrsUHx+vdevWafjw4XI6c1Y7TElJUZ8+feRyudS1a1e1a9eujK8OAAAAAAAAQEn5nSBdu3atLMvSPffcU2iZ++67T1JOj8y7777bO/y8LCxcuFDx8fHe1/Tp0yVJzz77rM/+nTt3euukpqZq+/bt2rdvX77zNW7cWBMnTpTNZtPDDz+sBg0aqG3btmrWrJk2bNigdu3a6YUXXshXLzo6WtOnT1d4eLheffVV1atXT+edd54aNmyohQsXKjExUZMnTy6z+wAAAAAAAACg9PxOkOauxt68efNCy+Q9dumllwYQVvGcTqdSUlK8r6ysLElSRkaGz/4Th70XpX///vr555/VrVs3HT9+XGvXrlWTJk00evRoLViwQJGRkQXWu+yyy7Rs2TL17t1blmUpOTlZtWvX1vDhw7VixYp8vU4BAAAAAAAAnBwskzuWvBg2m82b/GvZsmWx5VavXq2zzjoraIFWNWlpaYqJidGRI0cUHR1d0eEAAAAAQJUx9PMHdPj4YdWIqKH/3vBSRYcDACglf/NrfvcgLSmbrcxODQAAAAAAAABBQRYTAAAAAAAAQJVlL2mFUaNGKTY2NuBylmVp0qRJJW0eAAAAAAAAAIKmxAnSL774osjjlmX5VU4SCVIAAAAAAAAAFapECVI/13PyS24iFQAAAAAAAAAqit8J0q1bt5ZlHAAAAAAAAABQ7vxOkDZq1Kgs4wAAAAAAAACAcscq9gAAAAAAAACqLBKkAAAAQCl4jFG2yyNPEOfpBwAAQPkr8Sr2AAAAQFW2JzVLC/44ouRdx+R0GzlCLLWuH6kLm8eobmxYRYcHAACAEiJBCgAAAPhp1Y50fbJkvzx5Oo063UYrtqdr1Y509UpK0LkNoyouQAAAAJQYQ+wBAAAAP+xJzfJJjoY7bGpcK1zhjpxfqT1G+mTJfu1JzarAKAEAAFBS9CAFAAAA/LDgjyPe5Gi7xCh1bxOvULtN2S6Pvlh5UMu3pctjcsr1Skqo2GABAADgN3qQAgAAAMXwGKPkXcck5fQc7dE2XvYQS9kuj+whlrq3iff2JE3edYyFmwAAACoRepACAAAAxXC5jZzunKRnXJRdM5cfzLdIU81Iu/akZsvpNnK5jULtVgVHDQAAAH+QIAUAAACKYQ+x5Aix5HQb7T6crd2Hs73HchdpyuUIsWQPITkKAABQWTDEHgAAACiGzbJ0ekK4z74wu6XGtcIVdkJP0aYJEbJZJEgBAAAqCxKkAAAAQClYliUZ6cRcqBHzjwIAAFQmQRtin5aWps8++0yLFy/Wvn37lJGRocmTJ6tRo0beMnv27FFqaqrCw8PVpEmTYDUNAAAAlCmPMdq8P9NnX6bTo60HM/OV3bw/Ux5j6EUKAABQSQQlQTphwgSNHDlSR48elSQZY2RZlo4dO+ZT7n//+5/69u2r8PBw7dq1SzVr1gxG8wAAAECZyrtIU0J1u1we6dAxl/d4zUi7QmzSgaMuFmkCAACoZAIeYj969GgNGzZMaWlpCg0NVbt27Qote/PNN6tOnTrKysrSjBkzAm0aAAAAKBe5izRJ0v6jLp/kqJSTLD1wNGcfizQBAABULgElSFeuXKmxY8dKkvr166d9+/ZpyZIlhTdms+mmm26SMUZz5swJpGkAAACg3LBIEwAAwKkroATpa6+9JmOMLrjgAr3//vuKiYkpts4FF1wgSUpOTg6kaQAAAKDCeTxGOmFRJhZpAgAAqFwCmoP0f//7nyzL0tChQ/2uk5iYKEnavXt3IE0DAAAA5aagRZqyXEbbU7LylWWRJgAAgMoloB6ke/fulSS1aNHC7zphYWGSpKys/L9MAgAAACejvIs0FSd3kSYAAABUDgElSENDQyVJTqfT7zq5SdXY2NhAmgYAAADKjT3Ekv2E35zDHTYlxocp3OF7wG4TizQBAABUIgElSOvXry9JWrNmjd91vv/+e0lS06ZNA2kaAAAAKDc2y1J0xN+zU8VF2fXgVfV11yX19OBV9RUX9fex6Ag7w+sBAAAqkYASpJdeeqmMMXr33Xf9Kr9lyxZNmjRJlmXpH//4RyBNAwAAAOXGY4zSjru92ynpLr04e5femr9HL87epZR0l/dY2nG3PIYh9gAAAJVFQAnSoUOHym63a+HChRo9enSRZZctW6YrrrhC6enpCgsL05133hlI0wAAAEC5cbmNXB7fpGem06OtBzOV6fT4lvUwBykAAEBlElCCtHnz5nrsscdkjNHYsWPVoUMHPf/8897js2fP1n/+8x9ddtll6tChg7Zu3SrLsvTcc8+pTp06AQcPAAAAlAd7iCXHX/OKhtktndsg0rvtCMnZDrX/vc0cpAAAAJWHvfgiRXvsscfkdDr1zDPPaOnSpVq2bJmsv+Zceuihh7zljDGyLEuPP/64hg0bFmizAAAAQLmxWZZa14/Uiu3pynIZhYRYGnltQ9ksSx5j9NWqFGW7cnqNtq4fyRykAAAAlUjACVJJevLJJ3Xdddfpueee0+zZs5WRkeFzPDQ0VJdddplGjhypjh07BqNJAAAAoFxd2DxGq3aky2Ok5dvS9fuuYzotJlT7jmQr66/kqM3KKQcAAIDKIygJUkk677zz9Nlnn8nlcmnt2rXav3+/3G634uLi1KpVK0VERASrKQAAAKDc1Y0NU6+kBH38634ZSVkuo+0pWd7jlqReSQmqGxtWYTECAACg5IKWIPWe0G7X2WefHezTAgAAACcFy5IKWqSeUfUAAACVU0CLNB0+fDhYcQAAAAAntT2pWfpkyX7lLmYf7rApMT5M4Y6cX6k9RvpkyX7tSc0q4iwAAAA42QSUIK1Tp46uv/56ffrpp8rK4hdBAAAAnLoW/HHEmxxtlxilEd0a6q5L6mlEt4ZqlxglKSdJuuCPIxUYJQAAAEoqoARpdna2vvzyS/Xu3VsJCQm67bbbNHfuXJmCxhwBAAAAlZTHGCXvOiYpp+doj7bxCrXn/Codarepe5t4b0/S5F3H5OH3YQAAgEojoATpXXfdpbi4OBljdPToUb333nu68sorVa9ePT3wwANavnx5sOIEAAAAKozLbeR05yQ968SGyhHi+2t0qN2mOjGhkiSn28jlJkEKAABQWQSUIH399de1d+9eff311+rTp4+qVasmY4z27dun8ePHKykpSWeccYaeeuopbd68OVgxAwAAAOXKHmLJEZKzCtPe1Gxluzw+x7NdHu09ki1JcoRYsoewYhMAAEBlEVCCVJJCQkJ09dVX68MPP9Sff/6pqVOn6uqrr1ZISIiMMfrjjz/0xBNPqHnz5rrgggs0YcIEHThwIBixAwAAAOXCZllqXT9SkpTp9OiLlQe9SdJsV852pjNnu3X9SNlY0h4AAKDSsEwZTRiakpKiTz75RFOnTtXixYu985JalqWQkBBdfvnl+uabb8qi6VNCWlqaYmJidOTIEUVHR1d0OAAAAFXentQs/Xfubp9V7GtHO/RnmtObHLVZ0tDL66lubFgFRgogUEM/f0CHjx9WjYga+u8NL1V0OACAUvI3vxZwD9LCxMXFaciQIVqwYIG2bNmip59+Wq1atZIxRi6XS999911ZNQ0AAAAEXd3YMPVKSpDtr86hmU6Ptqdk+SRHeyUlkBwFAACoZOzl0UijRo10ww03KCMjQ7t371Zqamp5NAsAAAAEXWHjr1i4HgAAoHIq0wTp3r17NW3aNH300UdauXKlz7Hw8PCybBoAAAAIqj2pWfpkyX7l5kHD7JZOiw3VvtRsZbmMjKRPluxXQrSDXqQAAACVSNATpGlpafrss880depU/fTTT/J4PN75R202my655BL17dtXPXv2DHbTAAAAQJlZ8McR7/yj7RKj1L1NvELtNu8iTcu3pctjcsr1Skqo2GABAADgt6DMQZqdna3PP/9cPXv2VO3atXX77bfrxx9/lNvtljFGbdq00YsvvqidO3dqzpw5GjhwoKpXrx6MpvXNN9/o8ssvV82aNRUZGam2bdvqtddek8fjKdF5LMvy6/Xee+/51JsyZUqxdWbPnh2UawUAAEDF8Bij5F3HJOUsztSjbbzsIZayXR7ZQyx1bxOvcEfOr9bJu47Jw3h7AACASiOgHqTz5s3T1KlT9fnnnystLU2SvL1FExMT1adPH/Xr109nnHFG4JEW4LnnntOjjz4qSWrSpImioqK0evVqDRs2THPnztXMmTNls/mXA+7UqVOhxw4fPqy1a9dKks4///wCyyQkJKhZs2YFHqtRo4ZfMQAAAODk5HIbOd05v+fGRdk1c/lBJe86JqfbyBFiqXX9SNWMtGtParacbiOX2yjUblVw1AAAAPBHQAnSyy+/XJZleZOicXFx6tWrl/r27auOHTsGJcDCLF68WCNGjJDNZtOHH36oW265RZK0evVqXXnllfryyy81btw4Pfjgg36db8GCBYUeGzVqlNauXaukpCS1aNGiwDJdu3bVlClTSnwdAAAAOPnZQyw5Qiw53Ua7D2dr9+Fs7zGn22jF9nTvtiPEkj2E5CgAAEBlEfAQ+/DwcN1888368ssvtXfvXk2YMKHMk6OS9NRTT8kYo3/+85/e5KgknXPOORo3bpyknB6mTqczoHaMMZo6daok6dZbbw3oXAAAAKicbJal0xN8FxkNs1tqXCtcYSf0FG2aECGbRYIUAACgsggoQTplyhT9+eefmjZtmrp16ya7PehrPhUoLS1Nc+fOlSQNHjw43/GbbrpJ0dHRSklJ0fz58wNq6+eff9a2bdvkcDjUu3fvgM4FAACAU4dlWZKRTsyFGjH/KAAAQGUSUIK0f//+ioqKClYsflu5cqWys7MVHh6utm3b5jvucDjUvn17SdKvv/4aUFsffvihJOmqq65SfHx8oeVWr16tPn366NJLL1WPHj00ZswYbd68OaC2AQAAcHLwGKPN+zN99mU6Pdp6MFOZTt+E6Ob9mSzSBAAAUIkEZRX78rZx40ZJUsOGDQvttdqkSROfsqWRlZWlTz/9VFLxw+tXrVqladOmaf78+friiy80evRotWjRQk8//bTfbaWlpfm8AAAAcHLIu0hT3RqhatsoSo6/5hl1hFhq2yhKdWNDJcm7SBMAAAAqh/IZEx9khw8fllT06vC5x3LLlsZXX32l1NRUxcTE6Nprry2wTGxsrP71r3+pd+/eatq0qWJiYrRu3TqNGzdOH3zwgUaNGqWYmBgNHTq0yLaeffZZjRkzJt/+NWvWVEgvXQAAAPzNGCnEqia3sXTwSJYuqnVYZzSV3EYKsSS3SVdyWjVJlkIso/Vr1+Qbeg+g8nC5nN5/k5OTKzgaAEBppaenF19IfiZIQ0JCJOXMs+RyufLtL40Tz1USmZk5w5tCQ0MLLRMWFiZJOn78eKnakP4eXn/TTTcpPDy8wDI9evRQjx49fPade+65ev/99xUXF6fx48dr1KhRGjBggKpXr15oW48++qiGDx/u3U5LS1ODBg3UqlUrRUdHl/oaAAAAEBzrM/drxfZ0ZXssbcqure5t4hVqtynb5dEXKw/K6cn5BfychtV19tmnV3C0AAJh3+iQnJLd7lDr1q0rOhwAQCn5O0LbrwSpKWQOpcL2l7XcZGV2dnahZbKysiRJERERpWojJSVF33zzjaScuVZLY8yYMXrjjTd05MgRzZs3T927dy+0bFhYmDepCwAAgJPPhc1jtGpHujxGWr4tXWt2Z6hOTKj2HslWptMjSbJZOeUAAABQefiVIH3iiSdKtL+s+TN83p9h+EX5+OOP5XQ6lZiYqAsvvLBU54iOjlarVq20YsUKbdq0qVTnAAAAwMmhbmyYeiUl6JMl++Uxfy/SlMtmSb2SElQ3lj96AwAAVCaVMkHarFkzSdKOHTvkcrkKXKhpy5YtPmVLKnd4fb9+/WQFMIGUw+GQpFJPJwAAAICTx7kNo5QQ7dCCP44oedcxOd1GjhBLretH6sLmMSRHAQAAKqFKuUhTmzZt5HA4lJmZqRUrVigpKcnnuNPp1NKlSyVJHTp0KPH5N2/erMWLF0vKSZCWltvt1oYNGyRJ9evXL/V5AAAAcPLI7Ul6Y/uc1ertIZZsrMgEAABQadkCqfzTTz/pp59+KtFCSJmZmd56pRUdHa3LL79ckjRp0qR8xz/99FOlpaUpLi5OXbp0KfH5P/jgA0lSUlKSWrRoUeo4J02apNTUVIWEhJQqDgAAAJy8bJalULuN5CgAAEAlF1CCtEuXLrr00ku1detWv+vs3r3bWy8QI0eOlGVZeueddzRt2jTv/tWrV3tXg3/44Yd9VrofP368EhMT1bt37yLPPXXqVEnSrbfeWmS5tLQ03XLLLVqyZInPfrfbrYkTJ+ree++VJA0ePFj16tXz/+IAAAAAAAAAlIuAEqRS6VeyL229XJ06ddLYsWPl8XjUp08fnX766TrnnHPUtm1b/fnnn7rmmmv0wAMP+NRJTU3V9u3btW/fvkLPu3jxYm3atEkOh6PYRKrH49H06dPVoUMH1ahRQ23btlVSUpLi4+N1xx13KDMzU127dtUrr7wS0LUCAAAAAAAAKBsBJ0hLyuPxSJJCQkICPtfIkSP11Vdf6dJLL1VKSoo2bdqk1q1ba/z48friiy9K1Ubu8PqrrrpK8fHxRZaNjIzU888/rx49eig+Pl6bN2/WqlWrFB4ermuuuUYff/yxZs2apfDw8FJdHwAAAAAAAICyZZkAunLabDZZlqXk5GS1bNnSrzpz5szRlVdeqbi4OB04cKC0TZ/y0tLSFBMToyNHjig6OrqiwwEAAACAKmPo5w/o8PHDqhFRQ/+94aWKDgcAUEr+5tdKtIr9jh07Cty/d+9eRUVFFVk3KytLmzdv1mOPPSbLstSqVauSNA0AAAAAAAAAQVeiBGnjxo3z7TPG6Iorrihxw/379y9xHQAAAAAAAAAIphIlSAsbjV+SUfrh4eEaNmyYbrvttpI0DQAAAAAAAABBV6IE6bvvvuuzPWjQIFmWpbFjx6pevXqF1rMsS+Hh4apTp47atGlT7HB8AAAAAAAAACgPJUqQDhgwwGd70KBBkqQePXr4vUgTAAAAAAAAAJwsSpQgPdH8+fMlFTw3KQAAAAAAAACc7AJKkHbu3DlYcQAAAAAAAABAubNVdAAAAAAAAAAAUFEC6kGalzFGq1at0urVq3Xw4EEdP3682NXtH3/88WA1DwAAAJQrjzFyuY3sIZZsllXR4eAUNPGXKdp1ZHdFh1ElpWWlef994runKziaqqd+TD3dfv7Aig4DQBUSlATpe++9pzFjxmj79u0lqkeCFAAAAJXNntQsLfjjiJJ3HZPTbeQIsdS6fqQubB6jurFhFR0eTiG7juzWpoObKzqMKs3tcfMeAEAVEHCCdOTIkXruueeK7S0qSZZl+VUOAAAAOBmt2pGuT5bslyfPr7ROt9GK7elatSNdvZISdG7DqIoLEAAAACUW0Bykv/76q5599llJ0j/+8Q+tWrVKK1askJSTDHW73Tp48KBmz56t7t27yxijCy+8UHv37pXH4wk8egAAAKCc7EnN8kmOhjtsalwrXOGOnF+pPUb6ZMl+7UnNqsAoAQAAUFIBJUjfeOMNSVKjRo00a9YsnX322XI4HN7jlmWpZs2auuKKKzRz5kxNmDBBCxYs0FVXXaXs7OzAIgcAAADK0YI/jniTo+0SozSiW0Pd2aWuRnRrqHaJOb1GPSanHAAAACqPgBKkixYtkmVZGjZsmOz24kfrDxkyRD179tRvv/2m119/PZCmAQAAgHLjMUbJu45Jyuk52qNtvOwhlrJdHtlDLHVvE+/tSZq865g8TCsFAABQaQQ0B+nevXslSa1atfLus9n+zrk6nU6fHqWSdOutt2rGjBn6+OOPdd999wXSPAAAAFAuXG4jpzsn6RkXZdfM5QfzLdJUM9KuPanZcrpzVrcPtbOyPQAAQGUQUILU6XRKkhISErz7oqL+npT+wIEDqlu3rk+dBg0aSJI2bdoUSNMAAABAubGHWHKEWHK6jXYfztbuw39PF5W7SFMuR4glewjJUQAAgMoioCH2tWrVkiSlpaV599WuXVshISGSpHXr1uWrk9vr9OjRo4E0DQAAAJQbm2Xp9IRwv8o2TYiQzSJBCgAAUFkElCDNHVq/fv16777Q0FDv/o8//jhfnalTp0pSvp6lAAAAwMks0+kJajkAAACcHAJKkF500UUyxmj+/Pk++2+++WYZYzR58mQ9/vjjWrNmjZYuXaqhQ4dq2rRpsixLXbt2DShwAAAAoLx4jNGOlCy/ym5PyWSRJgAAgEokoARpjx49JElff/21zzD7e++9V4mJifJ4PHr66ad19tln6/zzz9cbb7whSapRo4YeffTRQJoGAAAAyk22yyOPnzlPj8kpDwAAgMohoEWaWrVqpfnz58vlcsnlcnn3V6tWTfPnz1e/fv20cOFCnzpnnXWWPvjgA9WvXz+QpgEAAIAK06ZhpLqdW1NZTqMwh6WvVx3Syh3HKjosAAAAlEJACVJJ6ty5c4H7GzVqpJ9//lkbNmzQmjVr5HK51KxZM7Vp0ybQJgEAAIBydeKq9Gt2Z/gkRENPOM4q9gAAAJVHwAnS4rRo0UItWrQo62YAAACAMuM5YcR8ttsUue3xKMDJrAAAAFBe+LUNAAAAKIY9xPL7F2eb6EEKAABQmZAgBQAAAIphsyzZ7f4lPe12SzaLBCkAAEBl4dcQ+/fff79MGu/fv3+ZnBcAAAAIJpfHo2yXf8vYZ7uMXB6P7Db6IgAAAFQGfiVIBw4cKCvIfwW3LIsEKQAAACqFzGxP8YVOKB8VToIUAACgMvD7tzZjTNBfAAAAQGUQHur7a/PZ9arJ8dc8o44QS2fXq1ZkeQAAAJy8/OpBunXr1rKOAwAAADhp2W021Yy069AxlyRp95FsPdS1vsIdIcp0uvXWj3u9ZWtG2hleDwAAUIn4lSBt1KhRWccBAAAAnNSubF1T037ZL0lKSXfpma93KtxuKfOEuUmvbF2zIsIDAABAKfGnbQAAAMAP5zSI0gWnR/vsOzE5esHp0TqnQVR5hgUAAIAA+dWDFAAAAIDUvW28EmuF67vkQ97h9lLOsPorW9ckOQoAAFAJBS1BmpaWps8++0yLFy/Wvn37lJGRocmTJ/sMz9+zZ49SU1MVHh6uJk2aBKtpAAAAoNyc0yBK5zSIksvjUWa2R+GhNuYcBQAAqMSCkiCdMGGCRo4cqaNHj0rKWfHesiwdO3bMp9z//vc/9e3bV+Hh4dq1a5dq1mR+JgAAAAAAAAAVJ+AE6ejRozV27FgZYxQWFqbWrVtr2bJlBZa9+eab9eCDD2rfvn2aMWOGbr/99kCbBwAAAMrV6p3pDLEHAAA4hQQ0FmjlypUaO3asJKlfv37at2+flixZUnhjNptuuukmGWM0Z86cQJoGAAAAyt0XKw5q2i/7fZKjknTomEvTftmvL1YcrKDIAAAAUFoBJUhfe+01GWN0wQUX6P3331dMTEyxdS644AJJUnJyciBNAwAAAOVq9c50Ld6cVmSZxZvTtHpnejlFBAAAgGAIKEH6v//9T5ZlaejQoX7XSUxMlCTt3r07kKYBAACAcjVrdUpQywEAAODkEFCCdO/evZKkFi1a+F0nLCxMkpSVlRVI0wAAAEC5cXk8Sjvuzrc/rIAZ/dOOu+XyeMohKgAAAARDQAnS0NBQSZLT6fS7Tm5SNTY2NpCmAQAAgHKTkeWbHA0NkRwhlrJcOf+GhhRdHgAAACevgBKk9evXlyStWbPG7zrff/+9JKlp06aBNA0AAACUG8syPtvZbsnpztnndBtlu4suDwAAgJNXQAnSSy+9VMYYvfvuu36V37JliyZNmiTLsvSPf/wjkKYBAACA8mOssi0PAACAChNQgnTo0KGy2+1auHChRo8eXWTZZcuW6YorrlB6errCwsJ05513BtI0AAAAUG4iwkKKLxRAeQAAAFScgBKkzZs312OPPSZjjMaOHasOHTro+eef9x6fPXu2/vOf/+iyyy5Thw4dtHXrVlmWpeeee0516tQJOHgAAACgPNiskvUILWl5AAAAVJyAEqSS9Nhjj2nUqFGyLEtLly7Vo48+KuuvXwgfeughjRgxQj/++KOMyZmH6fHHH9ewYcMCbdbrm2++0eWXX66aNWsqMjJSbdu21WuvvSZPCVcOHT16tCzLKvK1fv36QuuvW7dOffv2VZ06dRQeHq7TTz9dDz74oFJTUwO8QgAAAFS0bFfJfrcsaXkAAABUHHswTvLkk0/quuuu03PPPafZs2crIyPD53hoaKguu+wyjRw5Uh07dgxGk5Kk5557To8++qgkqUmTJoqKitLq1as1bNgwzZ07VzNnzpTNVrIccIMGDdSwYcMCj1WrVq3A/fPnz9c111yj48ePq1atWmrVqpXWr1+vl156STNnztSiRYtUu3btkl0cAAAAThruEv7xPac8w+wBAAAqg6AkSCXpvPPO02effSaXy6W1a9dq//79crvdiouLU6tWrRQRERGspiRJixcv1ogRI2Sz2fThhx/qlltukSStXr1aV155pb788kuNGzdODz74YInOe9tttxU7n2peR48e1c0336zjx49r2LBhevHFF+VwOJSSkqLu3btr4cKFGjx4sL7++usSxQEAAICTh8vtuyq9JckUsX1ieQAAAJy8Ah5ifyK73a6zzz5bl19+ua688kqdd955BSZHZ8yYEVA7Tz31lIwx+uc//+lNjkrSOeeco3HjxknK6WHqdDoDaqc4b775pg4cOKAzzzxT48aNk8PhkCTFxcXpo48+kt1u16xZs7RixYoyjQMAcHIyxiOTnSVjGG4LVGYhIb5zip6Y/jxx+8TyAADg5GA8Rs4sl4yHP2bib0HrQeoPY4w++ugjPfPMM9qwYYNcLlepzpOWlqa5c+dKkgYPHpzv+E033aQhQ4YoJSVF8+fP1xVXXBFQ3EX5/PPPJUkDBw5USIjvMKqGDRvq8ssv1+zZs/XZZ5+pbdu2ZRYHAODk4tq/U1lL5yp7wwrJmS05QhXaoq3C2l8ue0KDig4PQAnZSzhtU0nLAwCAspWyO1W//bhRW3/bJVe2W/bQEDU+u77O7tJMcfViKzo8VLBySZC6XC699957eu6557RlyxYZY7wLOZXGypUrlZ2drfDw8AKTjg6HQ+3bt9cPP/ygX3/9tUQJ0vnz52vNmjVKSUlRzZo1lZSUpP79++u0004r8LqWL18uSerUqVOB5+vUqZNmz56tX3/91e8YAACVW/baJTo2610p75yFzmxl//6LstcuUeQ1gxTaMqniAgRQYqH2kiU8S1oeAACUnU0rdmj+1KU+vUZd2W5tXLZdm1bs0CV926tp24LXo0HVUOIE6bFjx/T2229rzpw52rlzp+x2uxITE3XTTTfp5ptv9ulFaYzRlClTNGbMGO3cudO7LzQ0VAMHDix10Bs3bpSU00PTbi/4Epo0aaIffvjBW9ZfP/30k8/2jBkzNHr0aL3++uv5Yt62bZt3CH+TJk0KjSNvzIXJyspSVlaWdzstLa1EcQMATg6u/Tt9kqNWWDWFJNSXe/9OmazjksejY7PelS2+Dj1JgUrEZlmKDLPpWFbx02VEhdlkC6AzAAAACJ6U3ak+ydHQCIfi6sYoZfcRZWc6ZTxG86cuVY3a0fQkrcJKlCD97bffdPXVV2vv3r359n/55Zd644039O233yoqKkobN25Unz59vHNvGmMUHh6uwYMH65FHHlH9+vVLHfThw4clSTVq1Ci0TO6x3LLFqVOnjkaMGKHrr79eTZo0UUREhFauXKmnnnpK3377rW677TbFxcXp2muvzRdHUbH4G8ezzz6rMWPG5Nu/Zs0aRUVF+XUNAICKV2P1HEX+lRw9Vu8MpZ7VWSbEIcvtVOzv/1Pk7vWSx6N933+mw+dcXsHRAvCXMdKxrGrKWY6paOlZbv32W7LIkSJQGRkZFR0CUCEyMjKUnJxc0WHgFLHpp73e5GitZtFqfEFthdhtauCqoa2L/9SBjWkyHqOf/m+Jml5cp4KjRbClp6f7Vc7vBOmxY8fUvXt37dmzp9AyixYt0tChQ/XQQw+pc+fOOnz4sIwxioyM1J133qmHHnpItWvX9rfJQmVmZkqSQkNDCy0TFhYmSTp+/Lhf57zzzjvz7evYsaNmzZqlnj17aubMmbr//vvVrVs37/QAuXEUFYu/cTz66KMaPny4dzstLU0NGjRQq1atFB0d7dc1AAAqljEepc55W1JOz9F6N9+j+o6/Px9Mq7N0ZMIjMlkZity/RfXOaiXLYhguUBlkOt3Shu1+lrbU/MyWCneEFF8UKEK1PV9K5EhRBVWrVk2tW7eu6DBwCjAeo6UfbpKU03P02n9eJnvo35/PZ7Vy6cPRs5R93KnDO47prFZnybLxF85Tib8jtP3+X9n777+v7du3y7IsXXrppfrpp5+Unp6u48ePa/ny5erdu7eMMZo2bZp69+6tQ4cOybIs3X333dqyZYtefPHFoCRHJSk8PFySlJ2dXWiZ3OHqERERAbVlWZaee+45SdLmzZv122+/5YujqFj8jSMsLEzR0dE+LwBAJeN05izIJCkkob4sh+8fzyxHqEIS6v1VNjunPIBKwWNKttJtScsDAIDgczndcmW7JUlxdWN8kqOSZA+1K65uTE7ZbLdcTne5x4iTg989SL/66itJUvPmzTV79myfuT/btGmjjz76SKmpqZo9e7bWrFmj0NBQff7557r66quDHrQ/w9b9GYbvr+bNm6tmzZo6dOiQNm3apHPOOSffuQ8fPqw6dfJ3xQ5mHACAk5zDITlCJWe23Pt3yTizfZKk5q/9OWVDc8oDAAAAKBN2R4jsoSFyZbuVsueIXNku2UP/zme5sl1K2X0kp2xoiOyM/qiy/O5BmpycLMuyNHz48EIXRho5cqSknF6Xt912W5kkRyWpWbNmkqQdO3bI5XIVWGbLli0+ZQPl+Os/sXnbS0xM9O7Pba+s4wAAnLwsy6bQFm0lSSYrQxnffyTzV49S48zO2c7KmXIltEU7htcDAAAAZciyWWp8ds4aONnHnVowY6Vc2Tl5HVe2SwtmrFR25l+Lb59Tn+H1VZjfPUhTUlIkSS1atCi0zJlnnun9+rrrrgsgrKK1adNGDodDmZmZWrFihZKSknyOO51OLV26VJLUoUOHgNs7ePCg9u/fL0k+i0vZ7Xa1bdtWv/76qxYuXKhOnTrlq7tw4cKgxQEAOPmFtb9c2WuXSB6Psn9fLOfG1QpJqJfTo/Sv5KhsNoW1v6xiAwVQIm5P8avXB1IeAACUjbO7NNOmFTtkPEZ/LNmubcl7fFaxl3ISqa0707GtKvO760rugkTx8fGFlqlZs6b367p16wYQVtGio6N1+eU5K/9OmjQp3/FPP/1UaWlpiouLU5cuXQJub9y4cTLGKCYmRu3bt/c5dsMNN0iSpkyZIrfbd66KHTt2aO7cuZKknj17BhwHAODkZ09ooMhrBkm2nI9Yk5Uh186NPsnRyGsGyZ7QoAKjBFBSmdklS3iWtDwAACgbcfVidUnf9t7eodnHndq7+aBPcvSSvu0VVy+2AqNERSuzsX2FDcMPlpEjR8qyLL3zzjuaNm2ad//q1au9q8E//PDDPqvLjx8/XomJierdu7fPudasWaO7775ba9as8dmfmZmpZ555Rv/5z38kSY888ki+1ervuusuxcfHa926dRo+fLicfy24kZKSoj59+sjlcqlr165q165d8C4eAHBSC22ZpOoDRij0rAty5hqVJEeoQs+6IGd/y6SiTwDgpFMtrGRzkpW0PAAAKDtN2zbUpf2SVD0u0md/9bhIXdovSU3bNqygyHCyKNssZhnq1KmTxo4dq1GjRqlPnz4aNWqUoqKi9Pvvv8vj8eiaa67RAw884FMnNTVV27dvV2Jios9+p9OpN954Q2+88YZq1aqlhg1zvjHWrVunjIwMSdLgwYP173//O18c0dHRmj59urp166ZXX31V06ZNU8OGDb11ExMTNXny5LK5CQCAk5Y9oYHs1wxUtav756xW73Aw5yhQiYWElGxOspKWBwAAZWfTih2aP3WpjMf47D+ackzzPlwiYwxJ0iquxAnS119/XQkJCUEp9/jjj5e0eR8jR47UOeeco5dfflnLly/Xvn371Lp1aw0aNEhDhw5VSIh/f7lPTEzU2LFjtWjRIq1fv14bNmxQdna2EhISdPXVV+uf//ynrrzyykLrX3bZZVq2bJmeeuopzZs3T8nJyapXr56uv/56jRo1ihXsAaAKsyybFBpW0WEACFB6RsELgxZVPjy60vZFAADglJGyO9UnORoa4fCZg9R4jOZPXaoataMZZl+FWcYYU3wxyWazybKC+5fwE+fsxN/S0tIUExOjI0eOKDo6uqLDAQAAqNIOHcvS89/s9rv8w1fXU81I/jiCwDzx3dPadHBzRYcBlLum8adrzJUjKzoMnCLmT12qjcu2S5KaJzXShT3byB5q965i/8eSv461b6QufdoXdSpUQv7m10o01s8YE7QXAAAAUFlERzjKtDwAAAg+4zHa+tsuSTk9Ry/s2Vb20JwRHvZQuy7s2Uahf31mb1m9K98QfFQdfo/7mT9/flnGAQAAAJy07DabLEn+/LfJ+qs8AACoWC6nW67snNHLcXVjZA/1nYrRHmpXXN0Y7d18UK5st1xOtxxhTJFTFfn9rnfu3Lks4wAAAABOWh5j/EqOSjlJ1P9v787jo6rP/v+/z6zZCRDDGjYXQEAFBRdUUBCLqIBLFSwCrtW63Sj2xvIVKIrQutZWrCC44HLXDfWnRRaRFqEYBCkIYlgTUcSwZM+s5/fHmJEh2wxkMszk9Xw8eJA5n+t85jqEk5lc81n8pilLAy9PBQAAImOzW2VzWOV1+7T/+yJ53d7gCFJJ8rq92r+nKBDrsMpmD28vGyQePtoGAAAA6lHu8kQ1HgAANDzDYqjzae0lSe4Kj1a+s15ed2Djxao1SN2VgdfsLqe3l2Hhw82minHDAAAAQD2KKyLbXLS4wqe0pCglAwAAwnbawJO1bV2+TL+pb7/YrV0bvw/ZxV4KFFJ7DTg5xpkilhhBCgAAANQjMy2ycQWRxgMAgOho2S5TF93QNzg61F3h0Q/bC0OKoxfd0Fct22XGMEvEGu/cAAAAgHpYIhxXEGk8AACInpP6dFDzVhnauCJPOzZ8J6/bJ5vDqi6nt1evASdTHAUFUgAAAKA+fjPcLZqOLh4AAERXy3aZGji6rwZcf5a8Hp9sditrjiKIAikAAABQj0h3pGcHewAAjk+GxZDdSTkMoZj7AwAAANTDZo2s4BlpPAAAAGKHAilwHDD9fvldlTL9/linAgAAauD1RTZlPtJ4AAAAxA5jioEYcuXvVNEnC1W2dpVMt0uGw6nUs85Ts0tHyNmhc6zTAwAAAAAASHgUSIEYKf3PCu2b+7Tk8wWPmW6XSlctV+mafyn7lvuUds6A2CUIAACCLBHOu4o0HgAAALHDWzcgBlz5O0OKo5aUVCV17SlLcmogwOfTvrlPy5W/M3ZJAgCAILcnsinzkcYDAAAgdhhBCsRA0ScLg8XRtPMHKes3v5XF6ZTf5VLhgudVunKZ5POpaPH7yr7lvpjmCgAAJIs1soJnpPEAAACIHUaQAo3M9PtVtnaVpMDI0awxgeKoJFmczkCxNCUwkrQs93M2bgIA4DhQWuaJajwAAABihwIp0MhMj1um2yVJcuR0lsXhDGm3OJ1y5HQKxLpdMj3uxk4RAAAcoczjqz/oGOIBAEDj8Hv9Ki+plN/LYCT8gin2QCMz7A4ZDqdMt0vugp3yu1zBEaSS5He55M7fFYh1OGXYHTHKFAAAVEm1W6MaDwAAomv7+gJ98dEmlewvCx5Lb5mqfsN66sTeOTHMDMcDRpACjcywWJR61nmSJH95mQoXPC+/KzCitGoNUn9F4Ad2at/+MtgGFwCAmMtMd9YfdAzxAAAgela+s17LXlkTUhyVpJL9ZVr2yhqtfGd9jDLD8YIRpEAMNLt0hErX/Evy+VS6cpnK1/1HjpxOcufvChZHZbWq2ZDhsU0UAABIkhzWyEaERhoPAACiY/v6Am1euT3kmCPJJnelN/h488rtatMli5GkTRhD04AYcHboHNid/udfnvzlZarc+nVIcTT7lvvk7NA5dkkCAIAgty+yNUUjjQcAANHxxUebgl9nZKXqxumXa9xjI3Tj9MuVkZUabMs9LA5NDwVSIEbSzhmgdg8/qbT+F8v4eaMmw+FUWv+LA8fPGRDjDAEAQJUDJa6oxgMAgIbn9/pDptWPuG+QktKSJElJaUkace9Fwbbi/WVs3NSEMcUeiKGqkaTmTffI9LgDGzix5igAAMcdn8+MajwAAGh4lRXu4NeOJJuSUkM3QU5KSwqZbl9Z4VZKelKj5ojjA5UY4DhgWCyyOJMojgIAcJxqnuGoP+gY4gEAQMNLSv7l9dhd6VVlaWVIe2VpZchapIfHo2mhGgMAAADUwxLh2+ZI4wEAQMOz2CxKb/nLOqMLn1keLJJWllZq4TPLg20ZLVNlsfH63VQxxR4AAAAAAAAJqd+wnlr2yhpJUnFhmV75f/9ftV3sJanvsJ6xSA/HCUrjAAAAQD0iXQWHVXMAADg+nNg7R6eef2LIsSOLo6eef6JO7J3TmGnhOMMIUgAAADQ6v2mq3B0/O8WWVHgiit9f4lF6cpSSaUApDosshhHrNAAAiKrzr+6tNl2ylPvRJhUftqt9RstU9R3Wk+IoKJACAACg8ZW7/Xrkg92xTiNqnlnyfaxTCMvkKzsqzWmNdRoAAETdib1zdGLvHPm9flVWuJWU7GDNUQRRIAUAIIpM0y95PJLdLsPgDRgAIHLTf/WwMpMzYp0G0CgOVRTr5bWvxToNJDDDYsjusMmwMIMCv6BACgBAFHj3FciVu1Tureskj1uyO+To2kfOvoNly2YKDwAgfJnJGWqR0iLWaQBAXNu/55D++1medv73O3ndPtkcVnU+rb1OG3iyWrbLjHV6iDEKpAAANDD35i9U9tF8yX/Y+ooet9yb/iP35i+UOmy8HKf2i12CAIC4cqiiONYpAI2G/++Ihm3r8rX8tVyZfjN4zOv2KW/tbm1bl6+Lbuirk/p0iGGGiDUKpAAANCDvvoKQ4qjhTJE1u718+wpkuiokv19lH82XJasNI0nRpKU4LJp8ZcdYpxGROcv36McSb71xrTNsumVgu0bI6NilOFj6Ix78v0V/jHUKQKM6KevE+oOAMO3fc6hacfRwpt/U8tdy1bxVBiNJmzAKpAAANCBX7tJgcdTR81ylDBktw+6Q6XGrfPHrcm9aLfn9cuUuk23YuNgmC8SQxTDibnOg685ppb8s2VNv3K/PbhV31wYAQKL672d5tRZHq5h+UxtX5Gng6L6NlBWON3xkDABAAzFNf2DNUQVGjlYVRyXJsDsCj50pkiT31i8DGzgBiBttM526/uzsOmOuPztbbTOdjZQRAACoi+k3teOrgpBjjmS7WnduKUeSPeT49vUF9RZSkbgYQQocB0y/X6bHLcPukGHhcwsgbnk8gQ2ZJFmz2weLo1UMu0PW7HbyFuQF4jweyUEhBYgnZ3RIU3aGXSu/LdK63aXB4306pun8U5pRHAUA4Dji9fjk8/4yKCG9ZarKisq1d+d+WWyG0lumqmR/mSTJ5/XL6/HJ7qRU1hTxXQdiyJW/U0WfLFTZ2lUy3S4ZDqdSzzpPzS4dIWeHzrFOD0Ck7HbJ7pA8bvn2fSfT45ZstkAh1G6XvF759n33c6wjcAxA3Gmb6dSv+2Ur78dylVT6lZ5k0a/71T2yFAAAND6rNXQAUlUxVJL8XjPkcU3xaDookAIxUvqfFdo392nJ5wseM90ula5artI1/1L2Lfcp7ZwBsUsQQMQMwyJH1z5yb/qPTFe5iuf9Uf7SIsnrlmwOWdKaBTZqkuToeqYMgzdgQDwzDCPkbwAAcHzx+SJb0srn88ti4z16U8R3HYgBV/7OkOKoJSVVSV17ypKcGgjw+bRv7tNy5e+MXZIAjoqz72Dp52KJ/9BPgeKoJHndgceSZBhy9h0UowwBAACApsFmj2zTxEjjkTgYQQrEQNEnC4PF0bTzBynrN7+VxemU3+VS4YLnVbpymeTzqWjx+8q+5b6Y5goAAAAAAJDIGEEKNDLT71fZ2lWSAiNHs8YEiqOSZHE6A8XSlMBI0rLcz2X62eUaiCeu3KWSGdj90pJ5gmT7eZ1Rmz3wWJJMU67cZTHKEAAAAGgaPC5vVOOROBhBCjQy0+OW6XZJkhw5nWU5Ygdri9MpR04nVW79WqbbFdjd3pkUi1QBRMg0/XJvXSdJMpwpyrjp4WqbNBX97fcyXeVyb/1SKZfdyDqkAAAAQJT4vZENOIo0HomD38qARmbYHTJ+Loq6C3bK73LJ9Pvld1X+/LdL7vxdgViHU4bdEcNsAUTE45E8gTVHrdntA/e7YQncy4ZFht0ha3a7n2PdgXgAAAAAUeGLcEZmpPFIHIwgBRqZYbEo9azzVLpqufzlZfru4bvlO3RQptslw+GUNbO5/BVlkqTUvv1lWPgcA4gbdrtkd0get3z7vguMAD/sQw7z5+OBWEcgHgAAAEBUJKc6azxud1rlcfnCjkfii/vKy8cff6zBgwerRYsWSk1NVZ8+ffTss8/KH2HVf/369Xr44Yc1YMAAZWVlyW63Kzs7W0OHDtV7771X63kvvfSSDMOo88+iRYuO9TKRYJpdOiK4y7V3397glHvT7ZJ3395AkGGo2ZDhMcoQwNEwDIscXftIkkxXucoXvy7z5xGlpscdeOyqkCQ5up7J9HoAAAAgigyLUePxmoqjdcUj8cX1CNKZM2dq0qRJkqQuXbooLS1NGzZs0D333KOlS5fqvffekyWM0Xfbt29Xnz59go87d+6sTp06aceOHVq0aJEWLVqksWPHat68ebX2l52drZNPPrnGtubNmx/F1SHhGUZwI5da2wHEHWffwXJv/kLy++XetFqevA2yZrcLjCj9uTgqi0XOvoNimygAAACQ4I5mkyZHMrO8mqK4HbqyevVqPfTQQ7JYLHr99de1fft2bdiwQevWrVOrVq30wQcf6MknnwyrL9M01aZNG82aNUvff/+9duzYobVr16qwsFDPPvusDMPQyy+/rOeee67WPoYOHaqVK1fW+Ofss89uqMtGgij6ZKH08yhnW3brwFRbSbI7Ao8lye9X0eL3Y5MggKNmy85R6rDx0s8fqJmucnkL8kKKo6nDxsuWnRPDLAEAAAAAVeK2QPrII4/INE3dcsstGjVqVPD46aefHiyMzpw5U54wNsBo3769tm3bpgcffFBt2rQJHrdYLLrrrrt0++23S5LmzJnTwFeBpsj0+1W2dpUkyZKSqvbTn1Xn5/+hTs//Q52f/4fa//FZWVJSJUlluZ/LZJFoIO44Tu2n9LEPydHz3JAPQBw9zw0cP7VfbBMEAAAAmgC70yaFOznT+DkeTVJcFkiLi4u1dOlSSdLNN99crf3aa69VRkaG9u/fr+XLl9fbX1JSklJSUmptHzJkiCTp22+/PcqMgV+YHndwzVFHTmdZHE4ZFosszqSf/3bKkdMpEOt2BdcvBBBfAiNJxynzf55R5v/8RZn/84xSh41j5CgAAADQSAyLIavNGlaszWZlDdImLC4LpOvXr5fb7VZSUlLI2qFV7Ha7+vbtK0las2bNMT9fZWWlJCk5ObnWmA0bNmj06NG6+OKLNWLECE2bNk3bt28/5udG4jHsDhmOwM547oKd8rtcIe1+l0vu/F2BWIczZAdsAPHHMCyBe5kNmQAAAIBG5ff65fPUvCHTkbwen/xeZnA2VXH521peXp4kqUOHDrLZah7+3KVLl5DYY/GPf/xDktS/f/9aY7766iu98cYbWr58ud5//31NnTpVXbt21aOPPnrMz4/EYlgsSj3rPEmSv7xMhQueDxZJ/S5X4HFFmSQptW9/GWFsNAYAAAAAAEJVVkQ2IzPSeCSOuFxc4eDBg5Lq3h2+qq0q9mgtXrxYCxculCRNnDixWntmZqbuvvtuXX/99TrppJPUrFkzbdmyRU8++aReffVVTZ48Wc2aNdNdd91V5/O4XC65DhtJWFxcfEx54/jW7NIRKl3zL8nnU+nKZSpf9x85cjrJnb8rWByV1apmQ4bHNlEAAAAAAOKU0xnZjvSRxiNxxGWBtGrKu8NR+9RjpzMwhbmiouKonyc/P1833HCDJOnOO+/UhRdeWC1mxIgRGjFiRMixM844Q6+88opatmypp59+WpMnT9bYsWOVnp5e63M99thjmjZtWrXjX3/9tdLS0o76GnD8sl56tZyL3pbh98tfXqbKrV8H20yLRa5Lr9a3RaXSxo0xzBIAANTG40mWZJHH49FGXq8RBeXl5bFOAYiJ8vJyfq6iQXhc3pDHhlU6/apOciTZ5a70aMO7u2QeNgN/w3//y0ZNCaa0tDSsuLj8riclJUmS3O7ahz5Xjcasa93Quhw4cEBDhw5VYWGhBg4cqCeffDLiPqZNm6bZs2erqKhIn376qYYPr3004KRJkzRhwoTg4+LiYuXk5KhHjx7KyMg4qmvAca5XL5V27KQDb78i7097g4dtJ7RWi2tuVFq/82OYHICGYpp+yeOR7HbWIQUSzIe7d6vC65PdblevXifFOh0koJTvP5CokaIJSklJUa9evWKdBhJA6cFyrdUv+8OYPumrt3bJ5rTK66q+NukpJ3ZVWvPaN/FG/Al3hnZcFkjDmT4fzjT82pSWluqyyy7T5s2bdeaZZ+qDDz4IjkiNREZGhnr06KF169Zp27ZtdcY6nc6jeg7Er9L/rNC+uU9LvtAfyt6f9mrfC09Ifp/SzhkQm+QAHDPvvgK5cpfKvXWd5HFLdoccXfvI2XcwO9kDAAAAjcDuqLnsVVNxtK54JL64HMpy8sknSwpMgfd6vTXG7NixIyQ2XC6XS8OHD9eaNWt06qmnatGiRXVOja+P3R5Yv6K2PNE0ufJ3hhRHLSmpSuraU5bk1ECAz6d9c5+WK39n7JIEcNTcm79Qycsz5N70n0BxVJI8brk3/SdwfPMXsU0QAAAAaAIcyZGtKRppPBJHXBZIe/fuLbvdrsrKSq1bt65au8fjUW5uriTp7LPPDrtfr9erX//61/r000/VpUsXLVmyRFlZWUedp8/n09atWyVJ7du3P+p+kHiKPlkYLI6mnT9IHZ58SW3/d4Y6PPWS0s4fFAjy+VS0+P3YJQngqHj3Fajso/mS3y9JMpwpsuWcIsP585Ivfr/KPpov776CGGYJAAAAJD7DYqhZdniD3jKz02VYjChnhONVXBZIMzIyNHjwYEnSiy++WK39rbfeUnFxsVq2bKmBAweG1adpmho3bpw++OADtW3bVkuXLlXbtm2PKc8XX3xRhw4dktVqDTsPJD7T71fZ2lWSAiNHs8b8Vpafl1ewOJ3K+s1vZUkJjCQty/1c5s9FFgDxwZW7NFgcdfQ8V81+N0vpo+9Xs9/9SY6e5waC/H65cpfFMEsAAACgafC6wpvR63Ez87cpi8sCqST94Q9/kGEYmjt3rt54443g8Q0bNgQ3O3rwwQdDdrp/+umn1alTJ11//fXV+rv33nv12muvKSsrS0uXLlXnzp3rzaG4uFijRo3SF1+ETpX0+XyaM2eO7r33XknSzTffrHbt2h3VdSLxmB63THdgEzFHTmdZHE6Zfr/8rkqZfr8sTqccOZ0CsW6XTE/tm5EBOL6Ypj+w5qgCI0dThoyWYQ+8Dhl2R+CxM7Dou3vrl4ENnAAAAABEhd/rV1lRRVixZYcq5Pfy/rypitvVZ/v376/p06dr8uTJGj16tCZPnqy0tDRt2rRJfr9fw4YN0/333x9yzqFDh7R792516tQp5Pjq1av17LPPSgrsen/rrbfW+rwrV64Mfu33+/Xmm2/qzTffVGZmpjp37iybzaa8vDwdOnRIkjR06FA988wzDXPRSAiG3SHD4ZTpdsm1e7t+fP5xla9fI9PtkuFwKqX32XLtDqyhazicweIKgDjg8QTXHLVmt692/xp2h6zZ7eQtyAvEeTySgw36AAAAgGiorIhswFFlhVsp6UlRygbHs7gtkEqBUaSnn366nnrqKX355Zfau3evevXqpfHjx+uuu+6S1WoNqx+XyxX8uqCgQAUF4a0Ll5qaqj/96U9atWqVNm3apO3bt6uiokItW7bUsGHDdOONN+raa6+VYbCGBX5hWCxKPes8la5aLrOyQmVr/hVsM92ukMepffvLsMTtQG+g6bHbJbtD8rjl2/edTI87pEhq/nw8EOsIxAMAAACICqez5vfb9iSbPJXVp9TXFo/EF9cFUkm6/PLLdfnll4cVO3XqVE2dOrXa8YEDB8o0zYif2263a+LEiRGfB6Sc3lelq5aHHDOSU2RWlIfGnXZWY6YF4BgZhkWOrn3k3vQfma5ylS9+PTjN3vS4Vb74dZmuwBQfR9czZRh8AAIAAABEi/+IWo9hMXTtg5cos1WGDv1YrLf+tESm3wyJD2+oHRJN3BdIgXhUviG32rEji6OSVP7ftUrrd35jpASggTj7DpZ78xeS3y/3ptXy5G2QNbtdYETpz8VRWSxy9h0U20QBAACABGc5Ykav6Tf1j5mLZXfa5Klh86Yj49F0MHQFaGSH72JvJCUp9dwBMn5eg9BwOJV6zgAZSYE1T9jFHog/tuwcpQ4bL/28PIbpKpe3IC+kOJo6bLxs2TkxzBIAAABIfC6Xp8bjNRVH64pH4mMEKdDIDt/F3tnxJLW67X6Zfn9wrULDYtH3MyepcuvXwV3sDSeLRAPxxHFqP5kWiypXvCf/ocLgcUtmlpIGjJSjG8tnAAAAANGWlBzZpseRxiNxMIIUaGRVu9hLkrtgp/wulwyLRRZnkgyLRX6XS+78XYFYdrEH4pJ78xcq//DFkOKoJPkPFar8wxcDU/ABAAAARJXFZlFSmjOs2OR0pyw2ymRNFd95oJFV7WIvSf7yMhUueF5+V2BEqd/lCjyuKJPELvZAPPLuK1DZR/Oln5fHMJwpsuWcIsOZHAjw+1X20Xx59xXEMEsAAACgafD7wlu2Ltw4JCam2AMx0OzSESpd8y/J51PpymUqX/cfOXI6yZ2/K1gcldWqZkOGxzZRABFz5S4NFkcdPc9V8iXXy5AhU6Yqlrwp96bVkt8vV+4y2YaNi22yAAAAQALze/1yV4S3rqir3CO/188o0iaK7zoQA84OnZV9y32S1SopMJK0cuvXIcXR7Fvuk7ND59glCSBipumXe+u6wANHkky/T0V/nahDT92jor9OlOn3So7AmsLurV/KNPmUGgAAAIiWijJXVOOROBhBCsRI2jkDZG/bQUWL3w/sVu92BXax79tfzYYMpzgKxCOPR/K4A1+7K+U5fK1Rj1uezbkhj+XxSI7w1kQCAAAAEBmLYUQ1HomDAikQQ1UjSf3j7pK/vFSWlDRZbNyWQNyy2yWbXfL+Mo3HcKbImt1evn0FMl0Vv8Ta7IF4AAAAAFHhi3Bd0UjjkTioxAAx5MrfqaJPFqps7apfRpCedZ6aXTqCEaRAHDIMiyxpzYK711syT1D6mP+VJSVN/vJSlbw6U/5DPwXa0jJlGKx0AwAAAESL3RlZ2SvSeCQOvvNAjJT+Z4X2zX1a8vmCx0y3S6Wrlqt0zb+Ufct9SjtnQOwSBBAx0/TLX3oo+Nh/6CcV/X2yrCe0le+nPZK78pe20oMyTT9FUgAAACBKbDZrVOOROPitDIgBV/7OkOKoJSVVSV17ypKcGgjw+bRv7tNy5e+MXZIAIufxSF5v6DF3hXx7tocURyUF4jzh7agJAAAAIHKVFe6oxiNxMIIUiIGiTxYGi6Np5w9S1m9+K4vTKb/LpcIFz6t05TLJ51PR4vcDu90DiA92u2R3/LJRU52xDtYgBQAAAKLIZo9wBGmE8UgcjCAFGpnp96ts7SpJgZGjWWMCxVFJsjidgWJpSmAkaVnu5zL9LBINxAvDsMjWoWtYsbYO3ZheDwAAAESRIymyAQmRxiNx8JsZ0MhMj1um2yVJcuR0lsXhDGm3OJ1y5HQKxLpdMsMZiQbguBGyU31dce7w4gAAAAAA0UWBFGhkht0h4+eiqLtgp/wuV0i73+WSO39XINbhlGF3NHaKAI6Safrl27MjrFjfd9tlmowQBwAAAKLF4/LWH3QM8UgcrEEKNDLDYlHqWeepdNVy+cvLVLjg+WprkPoryiRJqX37y7DwOQYQL0y3Swq36Gn6ZbpdMpzJ0U0KCe+dtT/pxyJmG8RCWaUv+Pdzy/bEOJump1Uzh64+64RYpwEAOI6ZfjOq8UgcFEiBGGh26QiVrvmX5POpdOUyla/7jxw5neTO3xUsjspqVbMhw2ObKIBj4uhxtpIuuV6G1yvTZlPlkjfl/npNrNNCgvmxyK38A676AxE1PlN8DwAAOA4ZFiPksc1pld/rl99nymI1ZLFZ5HX5ao1H00GBFIgBZ4fOyr7lPu2b+7Tk88lfXqbKrV//EmC1KvuW++Ts0DlmOQI4CkfsSu8u2Cb3Xx+UvB7JZpdSMuqMBwAAANBwjtyV/vBiqN9nyu/z1RmPpoO5u0CMpJ0zQOkXD5OOnEJvsSj94mFKO2dAbBIDcNQMb+gbLBXvDxRHpcDfxfvrjgcAAADQYHy+yNb8jzQeiYMRpECM/PD4w6r4+qvqDX6/SpZ8IO/3+WrzwB8bPS8Ax8Bul6w2yRfG4u5WGyNIAQB1at+sXaxTaLJ2Htgln98nq8Wqzi06xTqdJof/+2goNrtVFqshv6/+tUUtVoMRpE0YBVIgBg5+/G714qhhCdncpeLrr3Tw43fV/LKrGjc5AEfNMCwyktNklh6qPzYlXYbBRA4AQO1uPWdcrFNosu56934drDioDGeGpl36h1inA+AoRbqmKGuQNl38ZgbEwKEP3jzs0c8/gIPF0V9+IB/68PA4AMc70/SHVRyVJLPkoMxwd7wHAAAAELGqDZnCivWZ8nt5f95UUSAFGpnP7ZbpqjzsyJE/rH95bFZWyud2N0peAI6d6Y5sF+tI4wEAAACEr6y4IqrxSBwUSIFG5is6GNV4ALHj94ex9ugxxAMAAAAIn8fliWo8EgcFUqCRWZs1j2o8gNgxPRGOII0wHgAAAED4rJbIyl6RxiNx8J0HGpkR4Q/cSOMBAAAAAIDkN8Jbf/Ro45E4qLwAjcxfXhrVeACxY9idUY0HAAAAED6nwxHVeCQOCqRAY3MkRTceQMwYRoQjxCOMBwAAABA+04huPBIHv5kBjcysLI9qPIDYoUAKAAAAHD+SkiMbERppPBIHv5kBjczn80c1HkDs+CyRrVkUaTwAAACA8PnNCNcgjTAeiYMCKdDI/KXFUY0HEDvGoUNRjQcAAAAQPqs1wl3sI4xH4uA7DzQyIyUtqvEAYseXEdn9Gmk8AAAAgPAxgxPhokAKNDLD64lqPIDYsZRFtmZwpPEAAAAAwmcxItt1KdJ4JA4KpEBjczijGw8gZnxGhJ9QRxgPAAAAIHwuV2QDjiKNR+KgQAo0MrOyIqrxAGLH9PiiGg8AAAAgfDYjsrJXpPFIHHzngUYW6XgxxpcB8cPvdkU1HgAAAED4iotKoxqPxEGBFGhk3qIDUY0HEDteX2QFz0jjAQAAAITPGmHZK9J4JA6+80Aj81eURTUeQOwY5ZVRjQcAAAAQPpc7wjVII4xH4qBACjQyS4vWUY0HEDtmenpU4wEAAACEz/RFtmhdpPFIHBRIgcZWHOGU+UjjAcSMWRnZlPlI4wEAAABEwGJENx4JgwIp0Mh8tshuu0jjAcSOWXYwqvEAAAAAwmeJ8PfpSOOROGyxTgBoaix+M6rxAGIoKSW68UAd7hrUTulJ1linATSKkkqfPlhfGOs0AADHucrSyGZsRRqPxEGBFGhk5RXFEcdnRicVAA2tIsJNlyKNB+qQnmRVsxTe2gEAAFSpKK+IajwSB++igUZWuX5t5PH9LopSNgAa1MG90Y0H6lBS6Yt1CkCj4f87ACAcB34qimo8EkfcF0g//vhjPfnkk1q3bp1cLpe6du2q8ePH63e/+50slsjXjli9erVmzpypVatWqbS0VJ07d9aoUaM0ceJEJSUl1Xreli1b9Mgjj+jTTz/VwYMH1a5dO40cOVKTJ09WZmbmMVwhEk7+d9GNBxA7WzZGHn/RNdHJBU3OX5ftiXUKQKPq0MIZ6xQAAMe54sLSqMYjccT16rMzZ87UsGHDtGzZMjVv3lwnnXSSNmzYoHvuuUcjR46U3++PqL/XXntNF1xwgT744AM5nU51795d27Zt08MPP6wLL7xQ5eXlNZ63fPlynXnmmXr99dfl8/nUo0cP7d27V0888YTOPPNM/fjjjw1xuUgUkRbMKbAD8aMksiU0Io4HAAAAEDaf3xvVeCSOuC2Qrl69Wg899JAsFotef/11bd++XRs2bNC6devUqlUrffDBB3ryySfD7m/Xrl26+eab5fP59Kc//UkFBQVat26d8vLy1LVrV+Xm5urBBx+sdl5JSYmuu+46VVRU6J577tGePXv05ZdfKj8/X/3799eOHTt08803N+SlI955PdGNBxBDNX+Q1nDxAAAAAMLlrohsSZZI45E44rZA+sgjj8g0Td1yyy0aNWpU8Pjpp58eLIzOnDlTHk94xaU///nPcrlcGjJkiCZOnCjDMCRJHTt21Lx58yRJL7zwQrXRoM8//7x++uknde/eXU8++aTsdrskqWXLlnr99ddls9n00Ucfad26dcd8zUgQP0W45mCk8QBiKNLpnkwPBQAAAKKlsqIsqvFIHHG5BmlxcbGWLl0qSTWOzrz22mt1xx13aP/+/Vq+fLmGDBlSZ3+maeq9996rtb/zzjtP3bp10zfffKP3339ft912W7Dt3XfflSSNGzdOVqs15LwOHTpo8ODBWrRokd5++2316dMnsguNoT2PTIx1CmHz+/3xNcryYGHE8QUP3xOdXBqSzX5U6/7GUrvJf451CjFR/OrMWKcQNr/fL/niaZqLK+L4Q/P+GJVMGpTVFnf3d8aY/411CgDQZPlNv0pd8b2On9/0B/8urozPJXHSnGmyGPH1+t1ULXz601inEDa/3y+/N7LlDGOp5IfIRoSW5Pv09p8WRymbhmOxWeLu/fmI+y6OdQp1issC6fr16+V2u5WUlFRj0dFut6tv375atmyZ1qxZU2+BND8/Xz/88IMkqX///jXG9O/fX998843WrFkTLJB6vV59+eWX9Z63aNEirVmzJuzrOx64tm+NdQo4jKdgV6xTQALxfb8z1ingMOZP8bGxDpONAADhKnWV6o537ot1Gg2iqLIobq9l9tVPKyMpI9ZpIAz7dh+IdQo4zIEf4vNDERybuCyQ5uXlSQqM0LTZar6ELl26aNmyZcHYcPpzOp1q27Ztrf0dHisF1i2tmsJf1R7OeTVxuVxyuX4ZeVRcHNsbssMT82L6/ACip9md8TOCFAAAAEh0o6dcFusUgCYvLgukBw8elCQ1b9681piqtqrYcPrLzMwMrj0aTn+Hf11bLuHm8dhjj2natGnVjn/99ddKS0ur89yG1q1bN9lbZDXqcwKx4vF49M0338Q6jUbTrVs32dNr/9kJJJKmcn+XlydJstYbBySa8vJybdy4MdZpoAblXjYhPB5s2bJFKbaUWKeBenTr1k1pmXyf0DTE4v15aWl4S77EZYG0srJSkuRwOGqNcToDG19UVFRErb+q8+o6N9w8Jk2apAkTJgQfFxcXKycnRz169FBGRuNPi/AeiHCdTCAO2VpkyW63q1evXrFOpVH5S+r/4AiId5b05k3m/v73vj1SZaTr3wLxLyUlRb16nRzrNFCD4spiaXOss0D37t2ZYh8nSg/xoQISX1pmSkzen4c7QzsuC6RJSUmSJLfbXWtM1XT15OTkqPVXdV7VuYc/jjQPp9MZLKYeD358blasUwhb3G3SJMmzp0Dyh7Gin8Uqe7uc6CfUENikKW6ULvx7rFMIW/xt0iSZJQelyjDe5CalyIiX0bxs0nRca9Ws9g94j2emacpvxjoLWAzVOoPqeBev//ebgjRnmmZf/XSs02jy0pyNOxMRR2/pS/+JdQphi7dNmiSptKhc7vL6f6dwpNiU1iw+RvOySVPDi8sCaTjT1sOZhn9kf4cOHZJpmjW+Saypv8O/PnjwoNq0aXNMeRxPmmrhqDHtuHlk3UVSi1VdXnyv8RJCk9GUCkexUvTiNPkLv6+13ZLVVs1untKIGSGRXX3WCbFOAQBCWAwLIxeBCBzvhaNE8NafFutgHZsvNW+ToWsfrHuDbyS2+Co3/+zkkwNTafLz8+X11vwpwI4dO0Jiw+nP5XLp++9r/oW2pv46deoku90e0n4seaBp6fLie7K2qnlTMGurthRHgTjW7OYpcpx1sWQc8TJrWOQ462KKowAAAEAjuvbBIep54YkyLKED4gyLoZ4XnkhxFPE5grR3796y2+2qrKzUunXr1K9fv5B2j8ej3NxcSdLZZ59db38dOnRQ69attXfvXn3++ef69a9/XS3m888/r9afzWZTnz59tGbNGn3++efq379/WOcBVTrOfF6SVHHggNx5m+Q4uaeSW7SIcVYAGkLqoOuUOug6eb2V0sEDUvMWstmqL8UCAAAAIPrOG9lb543sLW+lV8UHS5XRPE22pLgsiyEK4nIEaUZGhgYPHixJevHFF6u1v/XWWyouLlbLli01cODAevszDEMjR46stb9Vq1bpm2++kd1u15VXXhnSdtVVV0mSXnrpJfl8odOl8/PztXTpUknS1VdfXf+FoclKbtFCzc6+kOIokIBstiTZTmhLcRQAAAA4DtiSbGrRJpPiKELEZYFUkv7whz/IMAzNnTtXb7zxRvD4hg0bgrvBP/jggyG7yz/99NPq1KmTrr/++mr9TZw4UQ6HQ4sXL9af//xnmWZg14Ddu3frpptukiTdcsstat26dch5v/3tb5WVlaUtW7ZowoQJ8ngCmwXt379fo0ePltfr1dChQ3XmmWc27D8AAAAAAAAAgGNmmFWVwDj06KOPavLkyZKkLl26KC0tTZs2bZLf79ewYcP0/vvvy2q1BuOnTp2qadOmacCAAfrss8+q9ffKK69o/Pjx8vv9ateunbKzs7Vp0yZ5PB6deeaZWrFihVJTU6udt2zZMl1++eWqrKzUCSecoA4dOmjLli0qLy9Xp06dtHr16mqF1foUFxerWbNmKioqUkYGC5wDAAAAAAAAkQi3vha3I0ilwCjSDz/8UBdffLH279+vbdu2qVevXnr66aerFUfDceONN+rf//63Lr/8clVUVGjz5s3q0qWLpk6dqpUrV9ZYHJWkQYMGae3atbr++utlGIY2btyoVq1aacKECVq3bl3ExVEAAAAAAAAAjSOuR5AmMkaQAgAAAAAAAEevSYwgBQAAAAAAAIBjwZZdx6mqgb3FxcUxzgQAAAAAAACIP1V1tfom0FMgPU6VlJRIknJycmKcCQAAAAAAABC/SkpK1KxZs1rbWYP0OOX3+/X9998rPT1dhmHEOh1EWXFxsXJyclRQUMCas0CC4f4GEhf3N5C4uL+BxMX93bSYpqmSkhK1bdtWFkvtK40ygvQ4ZbFY1L59+1ingUaWkZHBD2ggQXF/A4mL+xtIXNzfQOLi/m466ho5WoVNmgAAAAAAAAA0WRRIAQAAAAAAADRZFEiB44DT6dSUKVPkdDpjnQqABsb9DSQu7m8gcXF/A4mL+xs1YZMmAAAAAAAAAE0WI0gBAAAAAAAANFkUSAEAAAAAAAA0WRRIAQAAAAAAADRZFEgBAAAAAAAANFkUSIEoME1TK1eu1MSJE3XOOecoMzNTDodDbdu21dVXX63ly5fXeu7+/fv18MMPq0+fPkpPT5fT6VS7du3Ut29f3X333XrnnXfk9/sb8WqAxJKfn68JEyaoZ8+eSk1NVXJysjp06KDzzjtPEydO1CeffBISP3DgQBmGEfLH6XQqJydH1113nVavXl3n8+3cuVMPPPCAevXqpWbNmik5OVmdO3fW2LFjtWLFimheKoDDdOrUKeQ+tlgsysjIUE5Oji655BJNnjxZmzdvrvX8mn4WGIahlJQUdevWTXfffbfy8/NrPHfq1Kk1nnv4z5JVq1ZF69KBhBeN+5vXeiC2jryvDcNQcnKyTjzxRN100036+uuvQ+LHjRtX42vt4X9GjBghv9+vc889V4Zh6J577qkzh/z8fKWnp8swDP3zn/+UJL300ksyDEOdOnWK1qUjRtjFHoiCZcuWafDgwZIki8Wik046SampqcrLy1NpaakkafLkyZo+fXrIeRs3btQll1yiH3/8UZLUvn17tWnTRiUlJdq2bZu8Xq8kqaSkRGlpaY14RUBi+PTTTzVixAiVlJTIarUqJydH2dnZOnDggLZv3y7TNNWyZUsVFhYGzxk4cKBWrFihnJwcdejQQZJUWlqqb7/9VhUVFbJYLPrb3/6m3/72t9We75lnntHvf/97uVwuOZ1OnXzyybLZbNq2bVvwZ8GYMWM0d+5cORyOxvlHAJqoTp06affu3Tr55JOVnZ0tSaqsrFRhYaF2794djLv66qv197//XS1btgw5v6afBaZp6scff9SuXbvk8/mUkZGhZcuW6ayzzgo5d+rUqZo2bZoyMjLUq1ev4PFDhw5px44dqqiokGEYmj17tm6//fZo/RMACSsa9zev9UBs1XRfHzp0SHl5eXK73XI6nXrrrbd0xRVXSAoUSF9++WVlZ2fr5JNPrrHPCy+8UDNmzNDXX3+tPn36yOv16t///rfOO++8GuMvu+wy/fOf/9QNN9ygBQsWSAoUSMePH6+OHTtq165dDX/hiB0TQINbsmSJedJJJ5nPPfeceeDAgeBxl8tlTpo0yZRkSjI//PDDYJvP5zO7d+9uSjLPPvtsc9OmTSF9lpaWmm+99ZY5ePBgs7S0tNGuBUgURUVFZlZWlinJHDZsmLlr166Q9oMHD5ovvfSSedlll4UcHzBggCnJnDJlSsjx4uJic/To0aYk0+FwVOvvySefNCWZVqvVnDZtmllUVBRsq6ioMGfPnm2mpqaakszhw4dHdC1VOe3cuTOi84CmrGPHjqYkc/78+dXafvrpJ/Ppp58O/ozo1q2beejQoZCY2n4WmKZp5uXlmb169TIlmX379q3WPmXKFFOSOWDAgGptBw8eNEeNGmVKMpOTk83CwsKjvUSgyYrW/R3L13qgqavtvt67d685ePBgU5LZsmVLs6SkxDRN0xw7dqwpyRw7dmxY/Ve9Nnfv3t2srKys1r5gwQJTkpmVlWX+9NNPwePz5883JZkdO3Y82kvDcYop9kAU9OvXT1u2bNEdd9yh5s2bB487HA7NmDFDQ4cOlSTNmTMn2PbFF19oy5YtkqS3335bPXr0COkzNTVV11xzjZYsWaLU1NRGuAogsXz88ccqLCxURkaG/vGPf6hjx44h7ZmZmRo7dqw++uijsPpLT0/X3Llz1bp1a7ndbr377rvBto0bN+r3v/+9JOmFF17Qww8/rIyMjGB7UlKSfvvb3+qDDz6QxWLR+++/rxdeeKEBrhLA0cjKytK9996rtWvXqk2bNvrmm2903333hX3+SSedpJkzZ0qScnNzVVxcHPa5mZmZeuGFF2SxWFRRUaHPP/880vQB1OFY7m9e64HjT6tWrfTqq6/K6XRq//79WrJkyVH189BDD+nUU0/Vli1b9Mgjj4S0FRYWBn9OPPPMM8rKyjrWtBEHKJACUZCRkSGbzVZr+yWXXCJJ+vbbb4PHduzYISnwJq59+/bRTRBogqrusVNOOUUpKSkN0mdycnJwKm1eXl7w+KxZs+TxeHTJJZfopptuqvX8iy++WLfccosk6bHHHpPP52uQvAAcnY4dO+q5556TJC1YsEAFBQURnVvF7XZH9LxpaWlq0aLFUZ0LIDxHe3/zWg8cf1q3bh2cRn/4fRkJh8OhF198URaLRbNmzdLGjRuDbffdd58KCwt12WWXafTo0Q2SM45/FEiBGKisrJQUeMNVpeoT5/379wcLOQAaTtU9lpeXp0OHDjVYv+YRS3l7PB699957kqQ777yz3vPvuOMOSdKuXbv0xRdfNFheAI7OlVdeqbZt28rr9Wrx4sVhn7d27VpJgQ86Ix1psnv37uDax926dYvoXADhO9r7m9d64Phz5H15NM455xz97ne/k8fj0c033yyfz6dFixbptddeU1pammbPnt0AmSJeUCAFGplpmnrrrbckSf379w8eP//885WamirTNHXppZfq5Zdf1k8//RSrNIGEM2TIEFksFhUVFWnw4MF65513VFRUdEx9VlRU6Msvv5QUmGIrSRs2bFB5ebkkacCAAfX2cfrpp6tZs2aSVO8uuQCiz2Kx6Nxzz5UUmC5fF9M0tW/fPr3++ut64IEHJEmTJk0K+7mKior02Wef6aqrrpIkXXHFFerZs+dRZg6gPpHc31V4rQeOP3v37tW2bdsk/XJfHq0ZM2aoY8eOys3N1SOPPBLcjO2xxx4LbtqGpoECKdDI5syZo/Xr18vhcISsf5SZmannnntOVqtV27Zt07hx45Sdna0uXbpo1KhRmjdvnkpKSmKXOBDnTjnlFE2fPl2S9OWXX+qaa65R8+bN1a1bN40fP17/93//J5fLFXZ/JSUluvXWW7V3717ZbDaNHDlSkrRnzx5JUvPmzUPWIK6NYRjq3LmzJOm7776L9LIAREFOTo4kad++fdXapk2bJsMwZBiGLBaLWrVqpRtuuEEtW7bUG2+8oQkTJtTa74oVK4LnGoahzMxMXXTRRdq+fbtmzJiht99+O2rXBCCgrvv7SLzWA8efffv2acyYMXK5XGrevHlw+boqL7/8cshr7eF/apKWlqbnn39ekjR16lTt3r1b5513Xlijw5FYal8kEUCDW7dune69915J0iOPPKITTzwxpP3GG29Ur169NGvWLH344YcqLy/Xzp07tXPnTr355pt68MEHNXv2bF177bWxSB+Iew899JDOPfdcPf7441q6dKncbre2bt2qrVu36qWXXlKHDh308ssva+DAgdXOnTdvnpYuXSpJKi0t1bfffquKigoZhqHHH388+ItP1QcZkWymVhXLhyDA8aGuezInJydkRElRUZF27typrVu3avbs2TrvvPNqHXGSkZGhXr16BR9XVlZq165d2r9/v+bNm6ezzz5bF198cQNfDYDD1XV/81oPHH9mzJihuXPnSpIOHTqkvLw8ud1u2e12zZkzR+np6SHx2dnZwfVJw/WrX/1Kv/nNb7RgwQI5HA7NnTtXFgvjCZsaCqRAI9m5c6cuv/xyVVZWavTo0cGpeEfq3bu33nzzTXk8Hq1fv15ffPGFFi1apMWLF2v//v26/vrrlZWVpYsuuqiRrwBIDBdddJEuuugiVVRUaO3atVqzZo0+/vhjffbZZ8rPz9dll12mdevWVVsHsKCgILihg81m0wknnKChQ4fqnnvuCZleV/UmraysLOycqmKPfIM3cOBArVixotbzqn5RO9LOnTvVqVOnsJ8fQKjS0lJJCtmRuspNN92kqVOnhhyrqKjQtGnTNGvWLF1wwQXavHlzjYWT3r1767PPPqt2/P/+7/904403aujQoVq1apXOPPPMBrkOANXVdX/H4rUeQN3y8vKCGzE5HA61bt1aF154oe6//36dccYZ1eKHDh2ql156KeLnGTRokBYsWKA2bdqoe/fux5g14hEFUqAR7N27V5dccol++OEHDRs2TC+99FKtQ/yr2O129evXT/369dNdd92lr7/+WkOGDNH333+vGTNmUCAFjlFycrIuuOACXXDBBXrggQe0cuVK/epXv1JZWZmeeOIJzZkzJyR+ypQp1YoiNWnXrp0k6eDBgzp48GC9U+9M09TOnTtDzq3Sq1cveb3eauds3LhRxcXFOuuss+R0Oqu1JyUl1ZsngNrl5+dLCoxCCUdycrJmzpypTz/9VLm5uZozZ07IMjr1ue6665Sbm6snnnhC06dP18KFC48iawDhqOv+jsVrPYC6zZ8/X+PGjYt1GmgCGDMMRNmBAwd0ySWXaPv27RowYIDeeust2e32iPvp0aOH/vCHP0gSu18CUXD++ecH1xo6lnvs9NNPV0pKiiTVOfqzyoYNG4KbRVVtHFHl2Wef1cqVK6v96d27tyTprbfeqrG9devWR50/0NT5/f7gJir9+vWL6NxzzjlH0tH9DDnvvPOO+lwA4TmW+/twDflaDwA4PlAgBaKotLRUl112mTZt2qS+ffvqww8/VHJy8lH316VLF0mS2+1uqBQBHKYh7jG73a4RI0ZIkmbPnl1vfNWi8B07dtTZZ5991M8LoGEsXLhQe/fuld1u15AhQyI61+/3Swp8OBqpYzkXQHiO5f4+HK/1AJB4KJACUeJyuTR8+HCtWbNGPXr00KJFi+pcc+jgwYM1TqU93KpVqyQp4kWnAUiFhYUyTbPOmIa6x37/+9/Lbrdr8eLFmjdvXq1xy5cvD07l/9///V9ZrdZjel4Ax2b37t266667JAU2ToxkKqxpmsGRaVUftkSi6ufP0ZwLoH7Hcn/XhNd6AEgsFEiBKPD5fLr++uv16aef6sQTT9SSJUvUokWLOs9ZsWKFunbtqqeeekp79uwJaauoqNBf/vIXzZo1S5I0fvz4qOUOJKoFCxbojDPO0Jw5c7R///6QtkOHDunhhx/WggULJB37PXbaaafp0UcflSTddttt+uMf/6ji4uJge2Vlpf7+97/ryiuvlN/v1+WXX67bb7/9mJ4TwNErLCzUX/7yF5111ln64YcfdOqpp+rJJ58M+/yKigo9+OCDWrdunSTpN7/5TdjnmqapN954Q3/9618lSWPGjIkseQB1Otb7uza81gNAYmGTJiAK/vGPfwQ3WLBYLLr22mtrjGvTpo3eeustSZJhGNqxY4cmTJigCRMmqH379mrTpo1KS0u1e/dulZeXS5Kuv/563X333Y1yHUAiMQxD//3vf3XbbbfptttuU+fOnXXCCSfo4MGD2r17d3Ba/QMPPKCRI0ce8/NNnDhRVqtVkyZN0pQpUzRjxgydcsopstlsysvLC+6iO2rUKM2fP7/ejdsANIwZM2Zo7ty5kgKzPQoLC7Vr165g+7XXXqvnn3++xh2uJWnevHlaunRp8HFRUZF27NgRfJ2ePn16cD3RI61fv17nn39+8HFlZaV27twZnFZ/2WWX6f777z+m6wOasmO9vyPFaz3QdBUUFCgrK6vW9ssuu0yvvPJKI2aEY0WBFIgCl8sV/DovL095eXk1xnXs2DH49RVXXKHVq1frn//8p1asWKH8/Hxt3LhRktS6dWv17dtXY8eO1bBhw6KbPJCg7rzzTp122mn65z//qc8//1zfffedvvrqK9lsNnXs2FHnnnuubr311pDixbGaMGGChg8frr/97W9avHixdu3aJY/Ho1atWmnEiBG66aabdNFFFzXY8wGo3+Gvy2lpacrMzNTgwYN19tln64YbblD37t3rPL+goEAFBQXBxw6HQ61atdIVV1yh3/3ud7rgggtqPbe4uFiff/558LHValXz5s01ePBgjRkzRmPGjKGAAhyDY72/jwav9UDT5Pf7q81KO9zhI8oRHwyzvgXZAAAAAAAAACBBsQYpAAAAAAAAgCaLAikAAAAAAACAJosCKQAAAAAAAIAmiwIpAAAAAAAAgCaLAikAAAAAAACAJosCKQAAAAAAAIAmiwIpAAAAAAAAgCaLAikAAAAAAACAJosCKQAAAAAAAIAmiwIpAAAAAAAAgCaLAikAAABQj6lTp8owDBmGEetUwtapUycZhqFx48bFOhUAAIDjGgVSAAAARMVnn30WLCoahqH09HSVl5fXe15FRYWaNWsWcu5nn30W/YQBAADQJFEgBQAAQKMoLS3VwoUL6417//33VVxcHPV8Di/gUoAFAABouiiQAgAAIOqSkpIkSa+++mq9sVUxVecAAAAA0USBFAAAAFF35ZVXSpKWLFmivXv31hq3b98+LV68WJI0fPjwRskNAAAATRsFUgAAAETdkCFD1Lp1a/l8Pr3xxhu1xr3xxhvyer1q1aqVLrnkkkbMEAAAAE0VBVIAAABEndVq1ahRoyTVPc3+lVdekSSNHj1aVqs1rL6/+OIL3XrrrTrllFOUlpam1NRUdevWTb/73e+Ul5dXLX7Xrl0yDEMXXXRR8NhFF10UsimUYRh66aWXan3OyspK/fnPf1afPn2Unp6u9PR09evXT3/961/l9XrrzXnXrl36n//5H/Xo0UPp6elKSUnRySefrNtvv10bN24M67o//vhjDR06VCeccIJSUlJ0yimnaMKECfr+++/DOv/QoUN69NFHde6556p58+ay2+064YQTdOqpp2rkyJGaPXu29u3bF1ZfAAAA8cwW6wQAAADQNIwZM0ZPPfWU1q9fr6+//lo9evQIad+8ebPWrVsXjN2wYUOd/Xm9Xt1zzz2aPXt2tbatW7dq69atmjNnjv72t7/p1ltvbbDr+PHHH3XppZdWyy83N1e5ublavHixFi5cKIul5rEIr7zyim677Ta5XK6Q49u2bdO2bdv04osvavr06Zo0aVKtOdx333165plnQo7l5eXpqaee0muvvaaPP/64zmvYsmWLBg8eXK2YWlhYqMLCQm3ZskULFy6Uz+fTXXfdVWdfAAAA8Y4RpAAAAGgUvXv3Vs+ePSXVPIq06liPHj3Uu3fvevu7+eabg8XRoUOHasGCBfriiy+Um5urOXPmqEePHvJ4PLrtttv04YcfBs9r166dNm7cqHnz5gWPzZs3Txs3bgz5M2LEiBqf96qrrtKWLVt0zz33aMmSJfryyy/1+uuvq3v37pKkDz/8UHPmzKnx3I8++kjjxo2Ty+VSWlqapkyZon//+99avXq1nnjiCWVlZcnn8+mhhx6qsfArSU888USwONq2bVs9++yzWrNmjVasWKEHH3xQhw4d0jXXXKPy8vJa/+3GjBmj77//Xna7XXfeeac+/PBD5ebmas2aNXrvvfc0adIknXLKKbX/4wMAACQSEwAAAIiC5cuXm5JMSeb8+fNN0zTNWbNmmZLM9u3bmz6fLxjr9/vNnJwcU5I5c+ZM0zRNc/78+cHzly9fHtL322+/HWybM2dOjc9fUVFhXnzxxaYks1OnTqbH46k1vyP7P9KUKVOCsXa7vcb4/fv3m61atTIlmaeddlq1drfbbbZr186UZKalpZnr16+vFrNr1y6zTZs2piQzJSXF/Omnn0La9+7da6akpJiSzI4dO5o//PBDtT6WLVtm2my2YL5jx44Nad++fXuw7dlnn631mv1+v3ngwIFa2wEAABIFI0gBAADQaG644QZZLBZ99913WrFiRfD4Z599poKCAlksFt1www319vPYY49JkkaOHKlbbrmlxpikpCT99a9/lRRY8/Ozzz479guQdPfdd2vgwIHVjrdo0ULjx4+XJP33v/9VUVFRSPt7772nPXv2SJL+8Ic/6IwzzqjWR8eOHfXnP/9ZklReXq758+eHtL/88svBkaFPPPGEWrduXa2Piy++uM4lBfbu3Rv8+sILL6w1zjAMNW/evNZ2AACAREGBFAAAAI2mXbt2wc2RDp9mX/X1wIED1b59+zr72LNnj7788ktJ0q9//es6Y7t3766srCxJ0urVq48678PVVcA988wzg1/v3LkzpG3p0qWSAoXHm266qdY+rr32WjVr1izknCP7aN68uYYPH15rH3X136ZNm+DXdW1EBQAA0FRQIAUAAECjuvHGGyVJb7/9tioqKlRRUaF33nlHUmBtzPqsXbs2+PWoUaOq7T5/5J/CwkJJoSMnj0W3bt1qbWvRokXw65KSkpC2TZs2SZI6deqk7OzsWvtwOBzBNVirzqlStcN97969ZbPVvt/qGWecIYfDUWNb586ddcEFF0iSnnrqKfXo0UMPP/ywPv300zrXLQUAAEhUFEgBAADQqK666iqlpKSopKRE77//vhYuXKji4mIlJyfr6quvrvf8ffv2HdXzNlTxLyUlpda2w3eu9/l8IW0HDhyQJLVq1are56iaOl91TpWDBw9KUp0FVkmy2WwhxdojvfHGGzr33HMlSZs3b9b06dM1aNAgZWZmasCAAXr++edVWVlZb54AAACJoPaPnQEAAIAoSEtL08iRI/Xaa6/p1VdflWmakqQRI0YoPT293vMPLzy+9tprOu2008J63uNlPU3DMOqNqfo3iVYf7dq106pVq7Rs2TK9++67WrFihTZv3iyPx6N//etf+te//qXHH39cH3/8MbvZAwCAhEeBFAAAAI3uxhtv1GuvvabFixcHj4UzvV6SWrZsGfzaMAz17NmzwfOLhqoRneFM9f/xxx9DzqnSvHlz7d27N9heG6/XGxxtWpdBgwZp0KBBkqT9+/dr6dKleuGFF/Tpp59q+/btuu6667R+/fp6+wEAAIhnTLEHAABAoxs0aJDatGkjr9crr9erVq1aaciQIWGdW7U+p6SQAmukwhmF2ZCqCrm7du2qc5kAj8cTLEoeWfzt1auXJOmrr76S1+uttY8NGzbI7XZHlF/Lli113XXXadmyZbryyiuDz5OXlxdRPwAAAPGGAikAAAAandVq1ZgxY+R0OuV0OvWb3/xGVqs1rHNPOukknXrqqZKkN998U/n5+UeVQ1JSUvBrl8t1VH1EYvDgwZICU9/nzZtXa9zbb7+toqKikHOO7OPAgQP68MMPa+2jrv7DUTWqVFJwkysAAIBERYEUAAAAMTFr1ixVVlaqsrJSjz/+eETnTp48WZJUWVmpq666Sj/99FOtsS6XS88991y1TYfatGkT/Hr79u0RPf/RGDlypNq2bStJmjFjhjZs2FAtpqCgQA888ICkwGZQ48ePD2kfO3askpOTJUkTJkyocar9ihUr9MILL9Sax1dffaWvvvqq1nbTNLV06VJJgVG2nTp1qvO6AAAA4h1rkAIAACDujBo1Sp988olefvllffnllzr11FN1++23a8CAATrhhBNUVlam7du369///rfeffddHThwQDfeeGNIHx06dFD79u313Xff6fHHH1e7du3UtWtX2WyBt8itWrUKa9OocNntdr3wwgu64oorVFJSovPPP18TJ07UoEGDZLPZtGrVKs2cOTM4/f7xxx9XVlZWSB+tWrXS9OnT9cADD2jXrl0688wzNWnSJPXr10+VlZX6+OOP9dRTT6ldu3YqLy+vsXD81Vdfafz48erbt6+uuOIK9enTR61bt5bH49HOnTs1f/58LVmyRJI0fPjwkEIyAABAIqJACgAAgLj04osvqlWrVnriiSdUWFioRx99VI8++miNsampqTVO4X/ooYd05513aufOnRoxYkRI2/z58zVu3LgGzXnYsGGaP3++br/9dpWWlmrKlCmaMmVKSIzVatX06dN1xx131NjH/fffr/z8fP3lL3/Rnj17dNddd4W0Z2Vl6e2339Y111xTZy65ubnKzc2ttf3888/Xiy++GOaVAQAAxC+m2AMAACAuWa1WzZo1S5s3b9b999+v3r17q3nz5rJarUpPT1ePHj10ww036OWXX9YPP/wQnJp+uDvuuEPvvPOOhgwZouzs7ODo0WgaO3asvvnmG917773q3r27UlNTlZycrBNPPFG33nqr1q9fr0mTJtXZxzPPPKOPPvpIl156qVq0aKGkpCSddNJJuueee7R+/XqdddZZtZ47evRoLV++XA899JAuuOACde7cWSkpKXI4HGrfvr2uvPJKvf7661qxYoVatGjR0JcPAABw3DFM0zRjnQQAAAAAAAAAxAIjSAEAAAAAAAA0WRRIAQAAAAAAADRZFEgBAAAAAAAANFkUSAEAAAAAAAA0WRRIAQAAAAAAADRZFEgBAAAAAAAANFkUSAEAAAAAAAA0WRRIAQAAAAAAADRZFEgBAAAAAAAANFkUSAEAAAAAAAA0WRRIAQAAAAAAADRZFEgBAAAAAAAANFkUSAEAAAAAAAA0WRRIAQAAAAAAADRZ/z+ljeTqwIHeoAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# draw boxplot of regret per instance\n",
        "fig = plt.figure(figsize=(16,6))\n",
        "boxplot_data = [df1[\"Relative Regret\"], df3[\"Relative Regret\"], df4[\"Relative Regret\"],\n",
        "                df5[\"Relative Regret\"], df6[\"Relative Regret\"]]\n",
        "bp = plt.boxplot(boxplot_data, medianprops=dict(color=\"dimgrey\", linewidth=2), patch_artist=True, widths=0.75)\n",
        "for i, patch in enumerate(bp[\"boxes\"]):\n",
        "    patch.set_facecolor(colors[i])\n",
        "    patch.set_color(colors[i])\n",
        "    patch.set_linewidth(4)\n",
        "for i, patch in enumerate(bp[\"whiskers\"]):\n",
        "    patch.set_color(colors[i//2])\n",
        "    patch.set_linewidth(2)\n",
        "for i, patch in enumerate(bp[\"caps\"]):\n",
        "    patch.set_color(colors[i//2])\n",
        "    patch.set_linewidth(3)\n",
        "for i, patch in enumerate(bp[\"fliers\"]):\n",
        "    patch.set_marker(\"o\")\n",
        "    patch.set_markeredgecolor(colors[i])\n",
        "    patch.set_markersize(6)\n",
        "    patch.set_markeredgewidth(2)\n",
        "for i, patch in enumerate(bp[\"medians\"]):\n",
        "    patch.set_color(\"w\")\n",
        "    patch.set_linewidth(2)\n",
        "# grid\n",
        "plt.grid(color=\"grey\", alpha=0.5, linewidth=0.5, which=\"major\", axis=\"y\")\n",
        "# labels and ticks\n",
        "plt.xticks(ticks=range(1,6), fontsize=16, labels=[\"2S\", \"SPO+\", \"DBB\", \"DPO\", \"PFYL\"])\n",
        "plt.xlabel(\"Methods\", fontsize=20)\n",
        "plt.ylabel(\"Relative Regret\", fontsize=20)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.xlim(0.5, 5.5)\n",
        "plt.ylim(-0.05, 1.8)\n",
        "plt.title(\"Relative Regret for each Instance on Test Set\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99234c01",
      "metadata": {
        "id": "99234c01"
      },
      "source": [
        "### 7.4 Path Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "442e2566",
      "metadata": {
        "id": "442e2566",
        "outputId": "9640bb65-11ad-4410-bc72-86f42887e669"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# draw boxplot of accuracy per instance\n",
        "fig = plt.figure(figsize=(16,6))\n",
        "boxplot_data = [df1[\"Accuracy\"], df3[\"Accuracy\"], df4[\"Accuracy\"], df5[\"Accuracy\"], df6[\"Accuracy\"]]\n",
        "bp = plt.boxplot(boxplot_data, medianprops=dict(color=\"dimgrey\", linewidth=2), patch_artist=True, widths=0.75)\n",
        "for i, patch in enumerate(bp[\"boxes\"]):\n",
        "    patch.set_facecolor(colors[i])\n",
        "    patch.set_color(colors[i])\n",
        "    patch.set_linewidth(4)\n",
        "for i, patch in enumerate(bp[\"whiskers\"]):\n",
        "    patch.set_color(colors[i//2])\n",
        "    patch.set_linewidth(2)\n",
        "for i, patch in enumerate(bp[\"caps\"]):\n",
        "    patch.set_color(colors[i//2])\n",
        "    patch.set_linewidth(3)\n",
        "for i, patch in enumerate(bp[\"fliers\"]):\n",
        "    patch.set_marker(\"o\")\n",
        "    patch.set_markeredgecolor(colors[i])\n",
        "    patch.set_markersize(6)\n",
        "    patch.set_markeredgewidth(2)\n",
        "for i, patch in enumerate(bp[\"medians\"]):\n",
        "    patch.set_color(\"w\")\n",
        "    patch.set_linewidth(2)\n",
        "# grid\n",
        "plt.grid(color=\"grey\", alpha=0.5, linewidth=0.5, which=\"major\", axis=\"y\")\n",
        "# labels and ticks\n",
        "plt.xticks(ticks=range(1,6), fontsize=16, labels=[\"2S\", \"SPO+\", \"DBB\", \"DPO\", \"PFYL\"])\n",
        "plt.xlabel(\"Methods\", fontsize=20)\n",
        "plt.ylabel(\"Path Accuracy\", fontsize=20)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.xlim(0.5, 5.5)\n",
        "plt.ylim(0.6, 1.02)\n",
        "plt.title(\"Path Accuracy for each Instance on Test Set\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9753fbb5",
      "metadata": {
        "id": "9753fbb5"
      },
      "source": [
        "### 7.4 Optimality Ratio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45710e88",
      "metadata": {
        "id": "45710e88",
        "outputId": "65f0ae5a-626a-4e45-b382-28b1d60f2220"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# draw barplot of optimality ratio per instance\n",
        "fig = plt.figure(figsize=(16,6))\n",
        "barplot_data = [df1[\"Optimal\"].mean(), df3[\"Optimal\"].mean(), df4[\"Optimal\"].mean(),\n",
        "                df5[\"Optimal\"].mean(), df6[\"Optimal\"].mean()]\n",
        "bp = plt.bar(range(5), barplot_data, color=colors)\n",
        "# grid\n",
        "plt.grid(color=\"grey\", alpha=0.5, linewidth=0.5, which=\"major\", axis=\"y\")\n",
        "# labels and ticks\n",
        "plt.xticks(ticks=range(5), fontsize=16, labels=[\"2S\", \"SPO+\", \"DBB\", \"DPO\", \"PFYL\"])\n",
        "plt.xlabel(\"Methods\", fontsize=20)\n",
        "plt.ylabel(\"Optimality Ratio\", fontsize=20)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.title(\"Optimality Ratio on Test Set\", fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6804030a",
      "metadata": {
        "id": "6804030a"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.15"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}